Machine Learning Services Benchmark - Inês Almeida @ PAPIs Connect
•
2 likes•949 views
The document compares Amazon ML, Google Predict, and Microsoft Azure ML for building machine learning solutions. It evaluates them on their abilities to perform data preprocessing operations like handling missing values and encodings, the types of algorithms they support, and their performance based on test accuracy. For most use cases, the document recommends building your own solution by exploiting your existing architecture and starting simple rather than using an external provider, unless a distributed solution is needed.
Report
Share
Report
Share
Download to read offline
Recommended
How to Build a Successful Data Team - Florian Douetteau @ PAPIs Connect
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve that.
As Data Manager, you know the challenges ahead:
Multitudes of technology choices to make
Building a team and solving the skill-set disconnect
Data can be deceiving...
Figuring out what the successful data product must be
The goal of this talk is to provide some perspective to these topics
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising and gaming industries, holding various data or CTO’s role. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains from the data enthusiasts and let them express their creativity.
Dataiku - google cloud platform roadshow - october 2013
This document discusses Hal's need for a big data platform at his company Dim's Private Showroom. It outlines Hal's wishes to better understand customer behavior, determine which products to feature, and solve data and computing challenges. The document then introduces Dataiku and its open source data tracking and mining platform using Google Cloud and Hadoop. Finally, it provides an example project timeline and discusses early successes including improved report times and optimization of marketing channels.
Eat whatever you can with PyBabe
This document discusses PyBabe, an open-source Python library for ETL (extract, transform, load) processes. PyBabe allows extracting data from various sources like FTP, SQL databases, and Amazon S3. It can perform transformations on the data like filtering, regular expressions, and date parsing. The transformed data can then be loaded to targets like SQL databases, MongoDB, Excel files, and more. PyBabe represents data as a stream of named tuples and processes the data lazily using generators for efficiency. Examples show how to use PyBabe to sort and join large files, send reports over email, and abstract ETL logic into reusable scripts.
How to Build Successful Data Team - Dataiku ?
How to build a successful data team ?
What are the challenges in terms of technology, people, product fit when building a data team ?
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Between traditional Business Intelligence and "Big Data" approaches, many companies need to innovate and work in a hybrid manner. How and with what tools can business and technical profiles collaborate productively together? lorian Douetteau, Dataiku's CEO, answers these questions.
Online Games Analytics - Data Science for Fun
This document discusses how a data analytics lab can help a small European online game company optimize their business using data science techniques. It provides examples of how the company could use analytics to improve marketing campaigns, predict customer value, analyze social gaming communities, and optimize their freemium business model. The document advocates establishing a small cross-functional data team with the right expertise, tools, and focus on experimentation to help drive business decisions with data and analytics.
Dataiku productive application to production - pap is may 2015
This document discusses the development of predictive applications and outlines a vision for a platform called "Blue Box" that could help address many of the challenges in building and deploying these applications at scale. It notes that building predictive applications currently requires integrating multiple separate components. The document then describes desired features for the Blue Box platform, such as data cleansing, external data integration, model updating, decision logic, auditing, and serving predictions in real-time. It poses questions about how such a platform could be created, whether through open source or a commercial offering.
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
This document provides an overview of big data and various big data tools including Pig, Hive, and Cascading. It discusses the history and motivation for each tool, how they work by mapping operations to MapReduce jobs, and compares key aspects of their data models, typing, and procedural vs declarative styles. The document is intended as a training presentation on these popular big data frameworks.
Recommended
How to Build a Successful Data Team - Florian Douetteau @ PAPIs Connect
As you walk into your office on Monday morning, before you've even had a chance to grab a cup of coffee, your CEO asks to see you. He's worried: both customer churn and fraudulent transactions have increased over the past 6 months. As Data Manager, you have 6 months to solve that.
As Data Manager, you know the challenges ahead:
Multitudes of technology choices to make
Building a team and solving the skill-set disconnect
Data can be deceiving...
Figuring out what the successful data product must be
The goal of this talk is to provide some perspective to these topics
Florian works in the “data” field since 01’, back when it was not yet big. He worked in successful startups in search engine, advertising and gaming industries, holding various data or CTO’s role. He started Dataiku in 2013, his first venture as a CEO, with the goal of alleviating the daily pains from the data enthusiasts and let them express their creativity.
Dataiku - google cloud platform roadshow - october 2013
This document discusses Hal's need for a big data platform at his company Dim's Private Showroom. It outlines Hal's wishes to better understand customer behavior, determine which products to feature, and solve data and computing challenges. The document then introduces Dataiku and its open source data tracking and mining platform using Google Cloud and Hadoop. Finally, it provides an example project timeline and discusses early successes including improved report times and optimization of marketing channels.
Eat whatever you can with PyBabe
This document discusses PyBabe, an open-source Python library for ETL (extract, transform, load) processes. PyBabe allows extracting data from various sources like FTP, SQL databases, and Amazon S3. It can perform transformations on the data like filtering, regular expressions, and date parsing. The transformed data can then be loaded to targets like SQL databases, MongoDB, Excel files, and more. PyBabe represents data as a stream of named tuples and processes the data lazily using generators for efficiency. Examples show how to use PyBabe to sort and join large files, send reports over email, and abstract ETL logic into reusable scripts.
How to Build Successful Data Team - Dataiku ?
How to build a successful data team ?
What are the challenges in terms of technology, people, product fit when building a data team ?
Dataiku - Big data paris 2015 - A Hybrid Platform, a Hybrid Team
Between traditional Business Intelligence and "Big Data" approaches, many companies need to innovate and work in a hybrid manner. How and with what tools can business and technical profiles collaborate productively together? lorian Douetteau, Dataiku's CEO, answers these questions.
Online Games Analytics - Data Science for Fun
This document discusses how a data analytics lab can help a small European online game company optimize their business using data science techniques. It provides examples of how the company could use analytics to improve marketing campaigns, predict customer value, analyze social gaming communities, and optimize their freemium business model. The document advocates establishing a small cross-functional data team with the right expertise, tools, and focus on experimentation to help drive business decisions with data and analytics.
Dataiku productive application to production - pap is may 2015
This document discusses the development of predictive applications and outlines a vision for a platform called "Blue Box" that could help address many of the challenges in building and deploying these applications at scale. It notes that building predictive applications currently requires integrating multiple separate components. The document then describes desired features for the Blue Box platform, such as data cleansing, external data integration, model updating, decision logic, auditing, and serving predictions in real-time. It poses questions about how such a platform could be created, whether through open source or a commercial offering.
BreizhJUG - Janvier 2014 - Big Data - Dataiku - Pages Jaunes
This document provides an overview of big data and various big data tools including Pig, Hive, and Cascading. It discusses the history and motivation for each tool, how they work by mapping operations to MapReduce jobs, and compares key aspects of their data models, typing, and procedural vs declarative styles. The document is intended as a training presentation on these popular big data frameworks.
Dataiku r users group v2
The document introduces building a data science platform in the cloud using Amazon Web Services and open source technologies. It discusses motivations for using a cloud-based approach for flexibility and cost effectiveness. The key building blocks are described as Amazon EC2 for infrastructure, Vertica for fast data storage and querying, and RStudio Server for analytical capabilities. Step-by-step instructions are provided to set up these components, including launching an EC2 instance, attaching an EBS volume for storage, installing Vertica and RStudio Server, and configuring connectivity between components. The platform allows for experimenting and iterating quickly on data analysis projects in the cloud.
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
This document discusses the challenges faced by a data team manager named Hal in developing a data science software platform for his company. It describes Hal's background in technical fields like functional programming. It then outlines some of the disconnects Hal experienced in determining the appropriate technologies, hiring the right people, accessing needed data, and involving product teams. The document provides suggestions for how Hal can find solutions, such as taking a polyglot approach using open source technologies, creating an API culture, and focusing on solving big business problems to gain support.
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
Dataiku - From Big Data To Machine Learning
This presentation was made in front of CIO to sensibilize to the big data in practical terms and to the new usages of machine learning and analytics.
Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Additional reading on connected can be found here: https://www.graphgrid.com/why-connected-data-is-more-useful/
The paradox of big data - dataiku / oxalide APEROTECH
The document discusses the paradoxes of big data. It notes that while data volumes are large, useful data can still be refined to fit in memory. It also discusses how the ecosystem around big data technologies like Hadoop and Spark has grown rapidly with many startups receiving funding. Practical uses of big data involve using tools like Dataiku's Data Science Studio to clean, model, and extract insights from multiple data sources to optimize processes like deliveries or improve search relevance. The document provides steps to get started with big data including learning Python/R and practicing on platforms like Kaggle to enter the field.
Better Insights from Your Master Data - Graph Database LA Meetup
Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and flexible structures as business needs are constantly changing.
We'll be discussing the benefits of using the Neo4j graph database for Master Data Management including the flexible schema free data model, concepts of layering in data, keeping your data current and flowing and then the benefits of connected data analytics and real-time recommendations that can result.
An overview of MDM with Neo4j https://www.graphgrid.com/graph-advantage-master-data-management/
The demo portion of the presentation is here: https://youtu.be/_GnDiwngnXk
Applied Data Science Course Part 1: Concepts & your first ML model
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Overview of Hadoop and its ecosystem: Pig, Hive, GraphLab, Mahout, Impala, Spark, Storm, ...
Presented at Epitech Paris on January 2014
The Rise of the DataOps - Dataiku - J On the Beach 2016
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
The 3 Key Barriers Keeping Companies from Deploying Data Products
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
The document discusses how Dataiku aims to help data scientists focus on real problems by providing a ready-to-use data science studio platform. The platform offers visual and interactive data preparation tools for data cleaning, guided machine learning for non-ML experts, and production-ready models and insights. Dataiku was founded in 2013 to make data science accessible to anyone by handling real-life data challenges through a common and democratic data science environment.
A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovations from HP Haven
Jeff Veis
Vice President
HP Software Big Data
Gilles Noisette
Master Solution Architect
HP EMEA Big Data CoE
democratization of data sql-konferenz
"Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.” Howard Aiken, Founder of Harvard’s Computing Science Program.
Data is moving so fast these days, and there is a shift whereby people are paying for value, not technology. This is where cloud computing comes in: it is very empowering, because anyone with an internet connection can access it. With Power BI in the cloud, small businesses are liberated with the ability to use the same tools and techniques to explore ideas as larger organisations.
In this session, we will look at understanding the Power BI components and tools available in the cloud, including the Power BI Admin Center, Power Query, Power Pivot, Power View and Power Map. We will look at how to use them will accelerate ideas and help to clarify decisions, and related to this, discuss the roles within IT and the business in relation to these tools. We will also look at business puzzles versus business mysteries, a definition evoked by Malcolm Gladwell (Blink, Outliers) in relation to Power BI.
“Out there in some garage is an entrepreneur who’s forging a bullet with your company’s name on it,” said Gary Hamel, a management guru. With Power BI, let’s see how you can translate your ideas in to a message that people can see, using cloud as an empowerment tool.
Mastering Customer Data on Apache Spark
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
Frank Bien Opening Keynote - Join 2016
Frank Bien, CEO of Looker - along with Amazon, Google and other data disrupters - discuss how innovators are deeply integrating analytics into every aspect of their businesses, from mobile to warehouse to cloud.
Frank shares Looker’s vision for the future of business intelligence and data analytics and reveal pivotal product and partnership updates.
Walmart Big Data Expo
The document discusses the role of humans in an era of big data and machine learning. It outlines that humans are needed to tag data to help machines understand it, and that crowdsourcing is one way to obtain tagged data at scale. The presentation also covers how the human-in-the-loop paradigm involves humans actively training machine learning models through techniques like active learning.
AI as a platform
This document discusses AI as a platform and options for AI adoption. It defines AI as learning, inference, and action. The global AI market is estimated at $13 trillion, with enterprise solutions ranging from $2.4-5 billion in funding. The document outlines a process for AI projects including data preparation, analysis, decision modeling, and acting on results. It provides examples of vendors that can help with different stages, such as DataRobot for end-to-end projects or individual tools for specific tasks. The appendix further explains machine learning workflows and includes examples of KNIME and DataRobot platforms.
Back to Square One: Building a Data Science Team from Scratch
Generally speaking, big data and data science originated in the west and are coming to Europe with a bit of a delay. There is at least one exception though: The London-based music discovery website Last.fm is a data company at heart and has been doing large-scale data processing and analysis for years. It started using Hadoop in early 2006, for instance, making it one of the earliest adopters worldwide. When I left Last.fm to join Massive Media, the social media company behind Netlog.com and Twoo.com, I basically moved from a data science forerunner to a newcomer. Massive Media had at least as much data to play with and tremendous potential, but they were not doing much with it yet. The data science team had to be build from the ground up and every step had to be argued for and justified along the way. Having done this exercise of evaluating everything I learned at Last.fm and starting over completely with a clean slate at Massive Media, I developed a pretty clear perspective on how to find good data scientists, what they should be doing, what tools they should be using, and how to organize them to work together efficiently as team, which is precisely what I would like to share in this talk.
H2O World - Data Science in Action @ 6sense - Viral Bajaria
Viral Bajaria, CTO and Co-Founder of 6sense, gave a presentation at H2O World about 6sense's predictive analytics platform. 6sense uses both first-party and third-party data to build hundreds of predictive models for different products. The presentation covered 6sense's data pipelines for preprocessing, modeling, and experimental modeling. 6sense's platform is built on AWS and uses Hadoop, Hive, Presto, and other technologies, with H2O and Scikit-Learn for machine learning.
Data Science at Speed. At Scale.
Every business is looking for a game-changer in data science, machine learning, and AI. Most organizations are also looking for ways to tap into open-source and commercial data science tools such as Python, RStudio, Apache Spark, Jupyter, and Zeppelin notebooks, to accelerate predictive and machine learning model building and deployment while leveraging the scale, security and governance of the Hortonworks Data Platform and other commercial platforms.
Ana Maria Echeverri will demonstrate how to accelerate data science, machine learning, and deep learning workflows by using IBM Watson Studio, an integrated environment for data scientists, application developers, and subject matter experts. This suite of tools allows to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale while using Open Source skills (i.e.: Python) and expanding into cognitive capabilities through access to Watson APIs to build AI-powered applications. If you love Python and want to tap into the power of IBM Watson, this is the session for you.
ENT301_Real-World AI For the Enterprise
Artificial Intelligence is here this time, to stay. For the Enterprise, AI materializes into solutions that improve customers' experiences by optimizing, automating, and personalizing high-volume tasks while lowering cost and time to market, therefore accelerating innovation. In this session, we cover AWS' AI products and services that enable innovation in the enterprise while maintaining compliance with different regimes such as HIPAA, PCI, and more. Finally, we discuss enterprise architectures on AWS for machine learning and deep learning workloads.
More Related Content
What's hot
Dataiku r users group v2
The document introduces building a data science platform in the cloud using Amazon Web Services and open source technologies. It discusses motivations for using a cloud-based approach for flexibility and cost effectiveness. The key building blocks are described as Amazon EC2 for infrastructure, Vertica for fast data storage and querying, and RStudio Server for analytical capabilities. Step-by-step instructions are provided to set up these components, including launching an EC2 instance, attaching an EBS volume for storage, installing Vertica and RStudio Server, and configuring connectivity between components. The platform allows for experimenting and iterating quickly on data analysis projects in the cloud.
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
This document discusses the challenges faced by a data team manager named Hal in developing a data science software platform for his company. It describes Hal's background in technical fields like functional programming. It then outlines some of the disconnects Hal experienced in determining the appropriate technologies, hiring the right people, accessing needed data, and involving product teams. The document provides suggestions for how Hal can find solutions, such as taking a polyglot approach using open source technologies, creating an API culture, and focusing on solving big business problems to gain support.
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.
Connected data is the key to your business succeeding and growing in today’s connected world.
Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement
The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4
Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/
Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com
Dataiku - From Big Data To Machine Learning
This presentation was made in front of CIO to sensibilize to the big data in practical terms and to the new usages of machine learning and analytics.
Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup
We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.
Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Additional reading on connected can be found here: https://www.graphgrid.com/why-connected-data-is-more-useful/
The paradox of big data - dataiku / oxalide APEROTECH
The document discusses the paradoxes of big data. It notes that while data volumes are large, useful data can still be refined to fit in memory. It also discusses how the ecosystem around big data technologies like Hadoop and Spark has grown rapidly with many startups receiving funding. Practical uses of big data involve using tools like Dataiku's Data Science Studio to clean, model, and extract insights from multiple data sources to optimize processes like deliveries or improve search relevance. The document provides steps to get started with big data including learning Python/R and practicing on platforms like Kaggle to enter the field.
Better Insights from Your Master Data - Graph Database LA Meetup
Master Data Management, is a practice that involves discovering, cleaning, housing, and governing data. Data architects for enterprises require a data model that offers ad hoc, variable, and flexible structures as business needs are constantly changing.
We'll be discussing the benefits of using the Neo4j graph database for Master Data Management including the flexible schema free data model, concepts of layering in data, keeping your data current and flowing and then the benefits of connected data analytics and real-time recommendations that can result.
An overview of MDM with Neo4j https://www.graphgrid.com/graph-advantage-master-data-management/
The demo portion of the presentation is here: https://youtu.be/_GnDiwngnXk
Applied Data Science Course Part 1: Concepts & your first ML model
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Overview of Hadoop and its ecosystem: Pig, Hive, GraphLab, Mahout, Impala, Spark, Storm, ...
Presented at Epitech Paris on January 2014
The Rise of the DataOps - Dataiku - J On the Beach 2016
Many organisations are creating groups dedicated to data. These groups have many names : Data Team, Data Labs, Analytics Teams….
But whatever the name, the success of those teams depends a lot on the quality of the data infrastructure and their ability to actually deploy data science applications in production.
In that regards a new role of “DataOps” is emerging. Similar, to Dev Ops for (Web) Dev, the Data Ops is a merge between a data engineer and a platform administrator. Well versed in cluster administration and optimisation, a data ops would have also a perspective on the quality of data quality and the relevance of predictive models.
Do you want to be a Data Ops ? We’ll discuss its role and challenges during this talk
The 3 Key Barriers Keeping Companies from Deploying Data Products
Getting from raw data to deploying data-driven solutions requires technology, data, and people. All of which exist. So why aren’t we seeing more truly data-driven companies: what's missing and why? During Strata Hadoop World Singapore 2015, Pauline Brown, Director of Marketing at Dataiku, explains how lack of collaboration is what is keeping companies from building and deploying data products effectively. Learn more about Dataiku and Data Science Studio: www.dataiku.com
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
The document discusses how Dataiku aims to help data scientists focus on real problems by providing a ready-to-use data science studio platform. The platform offers visual and interactive data preparation tools for data cleaning, guided machine learning for non-ML experts, and production-ready models and insights. Dataiku was founded in 2013 to make data science accessible to anyone by handling real-life data challenges through a common and democratic data science environment.
A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovations from HP Haven
Jeff Veis
Vice President
HP Software Big Data
Gilles Noisette
Master Solution Architect
HP EMEA Big Data CoE
democratization of data sql-konferenz
"Don’t worry about people stealing an idea. If it’s original, you will have to ram it down their throats.” Howard Aiken, Founder of Harvard’s Computing Science Program.
Data is moving so fast these days, and there is a shift whereby people are paying for value, not technology. This is where cloud computing comes in: it is very empowering, because anyone with an internet connection can access it. With Power BI in the cloud, small businesses are liberated with the ability to use the same tools and techniques to explore ideas as larger organisations.
In this session, we will look at understanding the Power BI components and tools available in the cloud, including the Power BI Admin Center, Power Query, Power Pivot, Power View and Power Map. We will look at how to use them will accelerate ideas and help to clarify decisions, and related to this, discuss the roles within IT and the business in relation to these tools. We will also look at business puzzles versus business mysteries, a definition evoked by Malcolm Gladwell (Blink, Outliers) in relation to Power BI.
“Out there in some garage is an entrepreneur who’s forging a bullet with your company’s name on it,” said Gary Hamel, a management guru. With Power BI, let’s see how you can translate your ideas in to a message that people can see, using cloud as an empowerment tool.
Mastering Customer Data on Apache Spark
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
Frank Bien Opening Keynote - Join 2016
Frank Bien, CEO of Looker - along with Amazon, Google and other data disrupters - discuss how innovators are deeply integrating analytics into every aspect of their businesses, from mobile to warehouse to cloud.
Frank shares Looker’s vision for the future of business intelligence and data analytics and reveal pivotal product and partnership updates.
Walmart Big Data Expo
The document discusses the role of humans in an era of big data and machine learning. It outlines that humans are needed to tag data to help machines understand it, and that crowdsourcing is one way to obtain tagged data at scale. The presentation also covers how the human-in-the-loop paradigm involves humans actively training machine learning models through techniques like active learning.
AI as a platform
This document discusses AI as a platform and options for AI adoption. It defines AI as learning, inference, and action. The global AI market is estimated at $13 trillion, with enterprise solutions ranging from $2.4-5 billion in funding. The document outlines a process for AI projects including data preparation, analysis, decision modeling, and acting on results. It provides examples of vendors that can help with different stages, such as DataRobot for end-to-end projects or individual tools for specific tasks. The appendix further explains machine learning workflows and includes examples of KNIME and DataRobot platforms.
Back to Square One: Building a Data Science Team from Scratch
Generally speaking, big data and data science originated in the west and are coming to Europe with a bit of a delay. There is at least one exception though: The London-based music discovery website Last.fm is a data company at heart and has been doing large-scale data processing and analysis for years. It started using Hadoop in early 2006, for instance, making it one of the earliest adopters worldwide. When I left Last.fm to join Massive Media, the social media company behind Netlog.com and Twoo.com, I basically moved from a data science forerunner to a newcomer. Massive Media had at least as much data to play with and tremendous potential, but they were not doing much with it yet. The data science team had to be build from the ground up and every step had to be argued for and justified along the way. Having done this exercise of evaluating everything I learned at Last.fm and starting over completely with a clean slate at Massive Media, I developed a pretty clear perspective on how to find good data scientists, what they should be doing, what tools they should be using, and how to organize them to work together efficiently as team, which is precisely what I would like to share in this talk.
H2O World - Data Science in Action @ 6sense - Viral Bajaria
Viral Bajaria, CTO and Co-Founder of 6sense, gave a presentation at H2O World about 6sense's predictive analytics platform. 6sense uses both first-party and third-party data to build hundreds of predictive models for different products. The presentation covered 6sense's data pipelines for preprocessing, modeling, and experimental modeling. 6sense's platform is built on AWS and uses Hadoop, Hive, Presto, and other technologies, with H2O and Scikit-Learn for machine learning.
What's hot (20)
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Dataiku - data driven nyc - april 2016 - the solitude of the data team m...
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...
Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and An...
Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup
Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup
The paradox of big data - dataiku / oxalide APEROTECH
The paradox of big data - dataiku / oxalide APEROTECH
Better Insights from Your Master Data - Graph Database LA Meetup
Better Insights from Your Master Data - Graph Database LA Meetup
Applied Data Science Course Part 1: Concepts & your first ML model
Applied Data Science Course Part 1: Concepts & your first ML model
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
Dataiku - hadoop ecosystem - @Epitech Paris - janvier 2014
The Rise of the DataOps - Dataiku - J On the Beach 2016
The Rise of the DataOps - Dataiku - J On the Beach 2016
The 3 Key Barriers Keeping Companies from Deploying Data Products
The 3 Key Barriers Keeping Companies from Deploying Data Products
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
Dataiku, Pitch Data Innovation Night, Boston, Septembre 16th
A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovation...
Back to Square One: Building a Data Science Team from Scratch
Back to Square One: Building a Data Science Team from Scratch
H2O World - Data Science in Action @ 6sense - Viral Bajaria
H2O World - Data Science in Action @ 6sense - Viral Bajaria
Similar to Machine Learning Services Benchmark - Inês Almeida @ PAPIs Connect
Data Science at Speed. At Scale.
Every business is looking for a game-changer in data science, machine learning, and AI. Most organizations are also looking for ways to tap into open-source and commercial data science tools such as Python, RStudio, Apache Spark, Jupyter, and Zeppelin notebooks, to accelerate predictive and machine learning model building and deployment while leveraging the scale, security and governance of the Hortonworks Data Platform and other commercial platforms.
Ana Maria Echeverri will demonstrate how to accelerate data science, machine learning, and deep learning workflows by using IBM Watson Studio, an integrated environment for data scientists, application developers, and subject matter experts. This suite of tools allows to collaboratively connect to data, wrangle that data and use it to build, train and deploy models at scale while using Open Source skills (i.e.: Python) and expanding into cognitive capabilities through access to Watson APIs to build AI-powered applications. If you love Python and want to tap into the power of IBM Watson, this is the session for you.
ENT301_Real-World AI For the Enterprise
Artificial Intelligence is here this time, to stay. For the Enterprise, AI materializes into solutions that improve customers' experiences by optimizing, automating, and personalizing high-volume tasks while lowering cost and time to market, therefore accelerating innovation. In this session, we cover AWS' AI products and services that enable innovation in the enterprise while maintaining compliance with different regimes such as HIPAA, PCI, and more. Finally, we discuss enterprise architectures on AWS for machine learning and deep learning workloads.
Intro to Big Data Analytics and the Hybrid Cloud
A high-level overview on Big Data, Big Data Analytics and the Hybrid Cloud. From Hadoop to Spark.
Follow Me on Twitter: @ibalina88
Machine Learning for everyone
This document discusses Amazon's use of machine learning across its businesses and services. It also discusses how Amazon uses machine learning to power product recommendations, customer support through self-service models, and optimizing call routing. Finally, it discusses how Amazon Machine Learning can help other companies build practical machine learning solutions to analyze data and gain business insights.
Aayush Saxena Resume
Aayush Saxena is a software developer currently working as a machine learning engineer at Jacobs in Singapore. He has a B.Tech in computer science from the International Institute of Information Technology in Hyderabad, India. His work experience includes roles as a software research engineer at the Singapore Management University and as a high frequency trading developer at Silverleaf in Mumbai, India. He has strong skills in Python, C/C++, Java, SQL, machine learning algorithms, and web development frameworks.
unit_5.pdf
AI cloud is a promising domain that has gained prominence for uses like data storage, processing, and software development. AI helps develop self-learning systems using machine learning algorithms trained on large datasets without requiring human programming. These AI clouds have been used in domains like self-driving cars, medical diagnosis, and speech recognition. Machine learning as a service (MLaaS) offers machine learning tools and APIs through cloud computing services, with computation handled by the provider's data centers. Popular MLaaS platforms offer services for natural language processing, computer vision, predictive analytics, and more.
IBM CDO Fall Summit 2016 Keynote: Driving innovation in the cognitive era
What does it take to drive Innovation in the Cognitive Era? Bob Picciano, Senior Vice President IBM Analytics and Inderpal Bhandari, Global Chief Data Officer, IBM gave this presentation to the CDOs and data professionals in attendance at the IBM Chief Data Officer Strategy Summit in Fall of 2016.
Learn more about the role of CDO: http://ibm.co/2cXasXy
Resume Darshan Kulkarni
Darshan Kulkarni is a graduate student seeking a position in information systems. He has a Master's degree in Information Systems from Santa Clara University and a Bachelor's degree in Computer Engineering from the University of Mumbai. He has experience in business analysis, database management, and embedded systems development. Currently, he is developing a smart instant messaging application for Santa Clara University students as his capstone project.
Resume Darshan Kulkarni
Darshan Kulkarni is a graduate student seeking a position in information systems. He has a Master's degree in Information Systems from Santa Clara University and a Bachelor's degree in Computer Engineering from the University of Mumbai. He has experience in business analysis, data modeling, dashboard creation, ETL processes, and embedded systems development using languages like Java, C, C++, Python, and SQL. Currently he is developing a smart instant messaging Android application as his capstone project at SCU.
How Cloud is Affecting Data Scientists
This document discusses how data science models have transitioned to the cloud to take advantage of greater computing resources. It notes that data science models are resource-intensive and traditionally required powerful local machines. The cloud allows data scientists to run models on cloud infrastructure for lower costs than high-end laptops and with access to many GPUs. Several major cloud platforms - Azure, AWS, and Google Cloud - are discussed and compared in terms of their machine learning offerings. The document also introduces Microsoft's Team Data Science Process, which aims to help data science teams collaborate more effectively on projects in the cloud.
How to leverage artificial intelligence in power apps with ai builder
Microsoft introduced Artificial Intelligence in PowerApps with an AI builder. You will need PowerApp developers well-versed with the AI builder functions.
The Big Picture on Big Data and Cognos
The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
Future of work machine learning and middle level jobs 112618
This essay presents a new framework to analyze the impact of AI and ML on work. Its premise is that AI and ML have already been adopted in many firms. Now, efforts are underway to simplify the next stage of adoption by removing the complex requirement to create well-formulated algorithms.
This innovation is automating the deployment of ML ecosystems. Early adopters report substantial gains in new revenues, additional efficiencies in operations and a changed mindset for employees. One example of the latter is LinkedIn’s efforts to establish a “culture of data,” where data serves as the foundation for corporate strategy and data analytics-based operations. This essay contends that by lifting earlier roadblocks to adoption, growth of ML and AI systems will increase, greater attention will be paid to obtaining and structuring data resources, and more ML systems can be applied to evaluating strategic and financial decisions.
Machine Learning On AWS
The document discusses machine learning on AWS. It describes three types of data-driven development: retrospective analysis, real-time processing and dashboards, and predictions. It provides examples of machine learning applications like fraud detection, personalization, targeted marketing, and content classification. The document also discusses best practices for building smart applications using machine learning, including defining problems, collecting and shaping data, training models, and evaluating performance. It highlights Amazon ML as a powerful scalable machine learning service and shares a case study of a company using it to classify medical symptoms.
MDM Architecture - SAP
This document outlines an MDM architecture using SAP components, including SAP MDG for the master data repository, SAP Info Steward for metadata management, and SAP Data Services for data integration and quality. It recommends using Sybase PowerDesigner for data modeling, profiling data with SAP Info Steward, and leveraging SAP HANA for faster processing. The architecture utilizes SAP components for presentation, persistence, integration and processing of master data.
Fyp final osama, saad, wajeeh
Commalytics is a platform that offers automated predictive analytics to help small e-commerce businesses grow. It was created in October 2017 when the founders realized no other platform provided this capability. The platform extracts data through an ETL process then analyzes it using components like time series in their analysis engine. This informs a formal dashboard and mobile app interface to optimize business processes. Their goal is to develop the best predictive analytics solution with input from customers and the community.
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices.Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Women in Big Data
Women in Big Data Forum’s mission is to strengthen the diversity in the big data field. As part of this initiative, they encourage and attract more female talent to the big data & analytics field and help them to connect, engage and grow.
Best Practices for Distributed Machine Learning and Predictive Analytics Usin...
This session, we focus on common use cases and design patterns for predictive analytics using Amazon EMR. We address accessing data from a data lake, extraction and preprocessing with Apache Spark, analytics and machine learning code development with notebooks (Jupyter, Zeppelin), and data visualization using Amazon QuickSight. We cover other operational topics, such as deployment patterns for ad hoc exploration and batch workloads using Spot and multi-user notebooks. The intended audience for this session includes technical users who are building statistical and data analytics models for the business using tools, such as Python, R, Spark, Presto, Amazon EMR, Notebooks.
Xpanse-Manufacturing-2023.pdf
The document discusses how AI, ML, and advanced analytics can be used to provide insights from industrial IoT data to solve business challenges. It notes that simply collecting and consolidating raw data provides limited value and that the data must be prepared for machine learning through activities like data cleaning, feature engineering, and model building which typically takes 3-4 months of effort by data scientists and engineers. The document then summarizes how Xpanse is able to rapidly deliver AI and analytics solutions in 5-10 days by leveraging their AI platform and expertise to prepare data and build models, providing faster time to value over traditional internal approaches.
Similar to Machine Learning Services Benchmark - Inês Almeida @ PAPIs Connect (20)
IBM CDO Fall Summit 2016 Keynote: Driving innovation in the cognitive era
IBM CDO Fall Summit 2016 Keynote: Driving innovation in the cognitive era
How to leverage artificial intelligence in power apps with ai builder
How to leverage artificial intelligence in power apps with ai builder
Future of work machine learning and middle level jobs 112618
Future of work machine learning and middle level jobs 112618
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...
NEW LAUNCH! Integrating Amazon SageMaker into your Enterprise - MCL345 - re:I...
Best Practices for Distributed Machine Learning and Predictive Analytics Usin...
Best Practices for Distributed Machine Learning and Predictive Analytics Usin...
More from PAPIs.io
Shortening the time from analysis to deployment with ml as-a-service — Luiz A...
The document discusses Tevec Systems' approach to machine learning as a service (MLaaS). It describes establishing separate data science and software engineering teams to develop models and pipelines. The teams collaborate using an agile data science process of continuous experimentation. This involves designing models at small/medium scale, then large scale testing on production frameworks before deciding whether to deploy in production. Establishing interfaces and software architecture standards from the start helps speed deployment with consistent results. The process has improved team growth and model performance incrementally for customers.
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017
Feature engineering is one of the most important, yet elusive, skills to master if you want to be a good data scientist. Machine learning competitions are hardly ever won with strong modeling techniques alone -- it is the combination of creative feature engineering and powerful modeling techniques that makes the difference. This tutorial will give the audience practical tips and tricks to improve the performance of machine learning algorithms. We will broadly look at feature engineering for applied machine learning, touching on subjects like: categorical vs. numerical variables, data cleaning, feature extraction, transformations, and imputation.
Extracting information from images using deep learning and transfer learning ...
For online businesses, recommender systems are paramount. There is an increasing need to take into account all the user information to tailor the best product offer, tailored to each new user.
Part of that information is the content that the user actually sees: the visuals of the products. When it comes to products like luxury hotels, pictures of the room, the building or even the nearby beach can significantly impact users’ decision.
In this talk, we will describe how we improved an online vacation retailer recommender system by using the information in images. We’ll explain how to leverage open data and pre-trained deep learning models to derive information on user taste. We will use a transfer learning approach that enables companies to use state of the art machine learning methods without needing deep learning expertise.
Discovering the hidden treasure of data using graph analytic — Ana Paula Appe...
Graphs are used to map relations on unstructured data. Companies’ data are most from database and mined using traditional data mining approach. However, model relational data as a graph can reveal useful insights and discovery relation among data that is ignored by traditional data mining techniques. In this work we used graphs to map physician relations using claim data as a proxy and this approach reveal interesting insights from health insurance company.
Deep learning for sentiment analysis — André Barbosa (elo7) @PAPIs Connect — ...
Convolutional Neural Networks (CNNs) are already proven to be the state of art technique for image classification projects. However, some recent research found that it can be also used for some text classification problems such as sentiment analysis.This talk presents some definitions about what CNNs are and shows a little bit code about how to build one in a little Sentiment Analysis project.
Building machine learning service in your business — Eric Chen (Uber) @PAPIs ...
When making machine learning applications in Uber, we identified a sequence of common practices and painful procedures, and thus built a machine learning platform as a service. We here present the key components to build such a scalable and reliable machine learning service which serves both our online and offline data processing needs.
Building machine learning applications locally with Spark — Joel Pinho Lucas ...
In times of huge amounts of heterogeneous data available, processing and extracting knowledge requires more and more efforts on building complex software architectures. In this context, Apache Spark provides a powerful and efficient approach for large-scale data processing. This talk will briefly introduce a powerful machine learning library (MLlib) along with a general overview of the Spark framework, describing how to launch applications within a cluster. In this way, a demo will show how to simulate a Spark cluster in a local machine using images available on a Docker Hub public repository. In the end, another demo will show how to save time using unit tests for validating jobs before running them in a cluster.
Battery log data mining — Ramon Oliveira (Datart) @PAPIs Connect — São Paulo ...
Motorola developed a new product and used machine learning to automatically analyze logs from users and customers to identify software bugs and suspicious behaviors. The system was trained over multiple weeks to improve its ability to accurately predict bugs and filter out false positives and negatives. Keywords were identified that helped determine whether issues were actual bugs or non-bugs, with some keywords like "tethering" being 92% accurate predictors of bugs.
A tensorflow recommending system for news — Fabrício Vargas Matos (Hearst tv)...
News recommendations are particularly challenging given the high number of new contents produced every day and the fast deterioration of its value for the users, demanding models and infrastructure able to deal with those nuances and serve a newly trained model about 100 times per day. Attending this presentation you're going to follow a detailed overview of how R&D team of Hearst's TV division is putting together Google BigQuery, Kubernetes cluster and Tensorflow to build a hybrid recommendation system combining model-based matrix factorization, content recency, and content semantics through NLP.
Scaling machine learning as a service at Uber — Li Erran Li at #papis2016
Machine learning as a service (MLaS) is imperative to the success of many companies as many internal teams and organizations need to gain business intelligence from big data. Building a scalable MLaS in a very challenging problem. In this paper, we present the scalable MLaS we built for a company that operates globally. We focus on several scalability challenges and our technical solutions.
Video at https://www.youtube.com/watch?v=MpnszJ_3Ong
Couldn't attend PAPIs '16? Get access to the other presentations' slides and videos at https://gumroad.com/products/fehon/
Real-world applications of AI - Daniel Hulme @ PAPIs Connect
This talk will offer answers to the following questions: What is data-driven decision making? What is AI? What is Business Intelligence? Why are these concepts important? What are the biggest challenges and opportunities?
Daniel is the CEO of Satalia that provides AI inspired solutions to solve industries hardest problems. He’s the co-founder of the ASI that transitions scientists into data scientists. Daniel has a MSci and EngD in AI from UCL, and is Director of UCL’s Business Analytics MSc; applying AI to solve business/social problems. Daniel has many Advisory and Executive positions, holds an international Kauffman Global Entrepreneur Scholarship and actively promotes innovation across the globe.
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...
Possibly the most important lesson we have learned after 60 years of AI research is that what seemed to be very difficult to achieve, such as accurate medical diagnosis to playing chess at the level of a Grand Master, turned out to be relatively easy whereas what seemed easy, such as visual object recognition or deep language understanding, turned out to be extremely difficult. In my talk I will try to explain the reasons for this apparent contradiction by briefly reviewing the past and present of AI and projecting it into the near future.
Ramon Lopez de Mantaras is Research Professor of the Spanish National Research Council (CSIC) and Director of the Artificial Intelligence Research Institute of the CSIC. Technical Engineer EE (Electrical Engineering) from the Technical Engineering School of Mondragón (Spain) in 1973. Master of Sciences in Automatic Control from the University of Toulouse III (France) in 1974, Ph.D. in Physics from the University of Toulouse III (France), in 1977, with a thesis in Robotics (done at LAAS, CNRS). Master of Science in Engineering (ComputerScience) from the University of California at Berkeley (USA) in 1979. Ph.D. in Computer Science, from the Technical University of Catalonia, Barcelona (Spain) in 1981.
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Everybody uses price promotions in retail. However, individual pricing is seldom used, particularly in offline retail. Marketing literature has been advocating the use of individual price discrimination for decades. Furthermore, product recommendations, ever-present in e-commerce, are also not often found in offline retail. We show the machine learning driven system behind a new promotion channel that enables retailers and manufacturers alike to target individual customers in offline retail. Lessons learned, technologies used, and machine learning approaches driving our system will be shown.
Daniel Guhl has a background in economics & marketing, and got interested in data modeling during his Ph.D.. Currently, he is working as a data scientist at a Berlin based Start-up and is pursuing a postdoc at Humboldt University. He enjoys learning everyday and focuses on solving real world problems.
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Deep Learning (DL) is becoming a big tsunami in the Machine Learning community. This talk aims at introducing DL, its motivation and main techniques. However, part of this talk is also devoted to demystify DL. What are the main advantages but also the main drawbacks of DL?. And what are the key issues that the practitioners have to consider?
Roberto Paredes is an Associate Professor at Departamento de Sistemas Informáticos y Computación DSIC of the Universidad Poliécnica de Valencia UPV. He belongs to the Pattern Recognition and Human Language Technologies Research Centre PRHLT. Roberto Paredes is the Director of the PRHLT and the President of the Spanish AERFAI Association. His main research interests are around the statistical learning, machine learning and more recently neural networks and deep learning.
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
The best services have one thing in common: a superb customer experience. Banking services are no exception to this rule, and indeed the quest for an effortless, well informed, and personalized customer experience is one of the main goals of today's innovation in digital banking services. According to what Maslow has described in his "pyramid of needs", customers are seeking a more intimate and meaningful experience where banking services can actively assist the customer in performing and managing their financial life. Predictive APIs have a fundamental role in all this, as they enable a new set of customer journeys such as automatic categorization of transactions, detecting and alerting recurrent payments, pre-approving credit requests or provide better tools to fight fraud without limiting legitimate customer transactions. In this talk, I will focus on how to provide better banking services by using predictive APIs. I will describe the path on how to get there and the challenges of implementing predictive APIs in a strictly audited and regulated domain such as banking. Finally, I will briefly introduce a number of data science techniques to implement those customer journeys and describe how big/fast data engineering can be used to realize predictive data pipelines.
Natalino is currently Enterprise Data Architect at ING in the Netherlands, where leads the strategy, definition, design and implementation of big/fast data solutions for data-driven applications, for personalized marketing, predictive analytics, and fraud/security management. All-round Software Architect, Data Technologist, Innovator, with 15+ years experience in research, development and management of distributed architectures and scalable services and applications.
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Fintech startups are taking business away from traditional institutions like banks, exchanges, and brokerages. One of the reasons that these startups are able to compete with $30B+ behemoths like Credit Suisse and Goldman Sachs is their advanced decision making capabilities. By leveraging new data sources and better predictive analytics, companies like Ferratum Bank can make more accurate decisions in a fraction of the time.
This talk will cover:
Types of decisions you can automate
Challenges in building predictive, financial apps
First-hand, real-world examples
Greg Lamp is the co-Founder and CTO of Yhat. In this role, Greg leads development of Yhat's core products and infrastructure and is the principal architect of the company's cloud and on-premise enterprise software applications. Greg was previously a product manager at OnDeck, a fintech startup in New York and before that an analyst at comScore. Greg is a graduate of the University of Virginia.
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
The document discusses developing general artificial intelligence (AGI) to help humanity and solve problems. It describes GoodAI's mission to develop AGI as fast as possible to help humanity understand the universe. The future envisioned includes using AGI for science, space exploration, saving lives, ending disease and tragedy. Short-term effects may include job replacement by intelligent machines, but long-term could include universal basic income and humans merging with AI/AGI technology. Challenges include risks from people, governments, and AGI technology itself.
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. In this talk we'll show how to use an AWS Spark cluster to train a model quickly from a laptop at a very little cost (around 10€).
Vincent Van Steenbergen is a freelance (big) data engineer who's working on a range of international projects, implementing systems able to handle terabytes of data, usually involving Spark, Scala, Kafka, Hadoop and Cassandra. His main interest right now is applying these techniques to solve machine learning problems. Vincent was previously a technical architect at Property. Works, a real estate startup in London and before that an R&D engineer at IDAaaS.
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
Shopping, or as the people on the other side of the counter call it, retail has become the number one breeding ground for predictive applications in the enterprise. What started as simple recommendation engines has evolved into a complex and powerful ecosystem of predictive applications that affect core processes such as pricing, replenishment and staff planning. In this talk, Ulrich Kerzel will share impact and experiences from building and operating predictive applications for large retailers, and explain why the future of retail is as much a science as an art.
Dr. Ulrich Kerzel is a Senior data scientists at Blue Yonder and renowned scientist with research experience at the University of Cambridge and CERN. Ulrich Kerzel earned his PhD under Professor Dr Feindt at the US Fermi National Laboratory and at that time made a considerable contribution to core technology of NeuroBayes. After his PhD, he went to the University of Cambridge, were he was a Senior Research Fellow at Magdelene College. His research work focused on complex statistical analyses to understand the origin of matter and antimatter using data from the LHCb experiment at the Large Hadron Collider at CERN, the world’s biggest research institute for particle physics. He continued this work as a Research Fellow at CERN before he came to Blue Yonder as a senior data scientist.
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
We live in a world of data, of big data. A big portion of this data has been generated by humans, and particularly through their mobile phones. In fact, there are almost as many mobile phones in the world as humans. The mobile phone is the piece of technology with the highest levels of adoption in human history. We carry them with us all through the day (and night, in many cases), leaving digital traces of our physical interactions. Mobile phones have become sensors of human activity in the large scale and also the most personal devices.
In my talk, I will present some of the work that we are doing at Telefonica Research in the area of human behavior understanding from data captured with mobile phones, and particularly our work in the area of Big Data for Social Good. I will highlight opportunities but also challenges that we would need to address in order to truly leverage this opportunity.
Nuria Oliver is a computer scientist and Scientific Director at Telefónica. She holds a Ph.D. from the Media Lab at MIT. She is one of the most cited female computer scientist in Spain, with her research having been cited by more than 8900 publications. She is well known for her work in computational models of human behavior, human computer-interaction, intelligent user interfaces, mobile computing and big data for social good.
More from PAPIs.io (20)
Shortening the time from analysis to deployment with ml as-a-service — Luiz A...
Shortening the time from analysis to deployment with ml as-a-service — Luiz A...
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017
Feature engineering — HJ Van Veen (Nubank) @@PAPIs Connect — São Paulo 2017
Extracting information from images using deep learning and transfer learning ...
Extracting information from images using deep learning and transfer learning ...
Discovering the hidden treasure of data using graph analytic — Ana Paula Appe...
Discovering the hidden treasure of data using graph analytic — Ana Paula Appe...
Deep learning for sentiment analysis — André Barbosa (elo7) @PAPIs Connect — ...
Deep learning for sentiment analysis — André Barbosa (elo7) @PAPIs Connect — ...
Building machine learning service in your business — Eric Chen (Uber) @PAPIs ...
Building machine learning service in your business — Eric Chen (Uber) @PAPIs ...
Building machine learning applications locally with Spark — Joel Pinho Lucas ...
Building machine learning applications locally with Spark — Joel Pinho Lucas ...
Battery log data mining — Ramon Oliveira (Datart) @PAPIs Connect — São Paulo ...
Battery log data mining — Ramon Oliveira (Datart) @PAPIs Connect — São Paulo ...
A tensorflow recommending system for news — Fabrício Vargas Matos (Hearst tv)...
A tensorflow recommending system for news — Fabrício Vargas Matos (Hearst tv)...
Scaling machine learning as a service at Uber — Li Erran Li at #papis2016
Scaling machine learning as a service at Uber — Li Erran Li at #papis2016
Real-world applications of AI - Daniel Hulme @ PAPIs Connect
Real-world applications of AI - Daniel Hulme @ PAPIs Connect
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...
Past, Present and Future of AI: a Fascinating Journey - Ramon Lopez de Mantar...
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Revolutionizing Offline Retail Pricing & Promotions with ML - Daniel Guhl @ P...
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Demystifying Deep Learning - Roberto Paredes Palacios @ PAPIs Connect
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
Predictive APIs: What about Banking? - Natalino Busa @ PAPIs Connect
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Microdecision making in financial services - Greg Lamp @ PAPIs Connect
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
Engineering the Future of Our Choice with General AI - JoEllen Lukavec Koeste...
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
Distributed deep learning with spark on AWS - Vincent Van Steenbergen @ PAPIs...
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
How to predict the future of shopping - Ulrich Kerzel @ PAPIs Connect
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
The emergent opportunity of Big Data for Social Good - Nuria Oliver @ PAPIs C...
Recently uploaded
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Operating System Used by Users in day-to-day life.pptx
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Digital Marketing Trends in 2024 | Guide for Staying Ahead
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Recently uploaded (20)
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Machine Learning Services Benchmark - Inês Almeida @ PAPIs Connect
- 1. Inês Almeida . PAPIs connect . March 2016 MLaaS Benchmark on building your ML solution
- 2. Inês Almeida . PAPIs connect . March 2016 ! given a user’s recent mobile app activity, will he return within two weeks?
- 3. Inês Almeida . PAPIs connect . March 2016 what is the best ML solution?
- 4. Inês Almeida . PAPIs connect . March 2016 Amazon ML + documentation + cheapest − model exporting + incremental training − unknown algorithms − model exporting + algorithm variety + interface − most expensive − model exporting Google Predict MS Azure ML
- 5. Inês Almeida . PAPIs connect . March 2016 aspects considered I. data preprocessing operations II. algorithms III. perfomance
- 6. Inês Almeida . PAPIs connect . March 2016 I. data preprocessing
- 7. Inês Almeida . PAPIs connect . March 2016 • turning raw data into structured data _ data cleaning _ missing value imputation _ feature engineering aka dark magic • can make or break your solution • probably easier to do on your side
- 8. Inês Almeida . PAPIs connect . March 2016 Amazon ML missing value imputation not explicity yes, automatic yes, custom yes no yes yes yes yes yes yes yes data scaling text tokenization categorical data encoding Google Predict MS Azure ML
- 9. Inês Almeida . PAPIs connect . March 2016 II. algorithms
- 10. Inês Almeida . PAPIs connect . March 2016 supervised learning • linear models _ easier to train and tune _ limited expressiveness • nonlinear models _ more expressive capabilities _ prone to overfitting _ random forests: the no-brainer
- 11. Inês Almeida . PAPIs connect . March 2016 Amazon ML supervised learning linear algorithms unknown, possibly linear linear and nonlinear algorithms none none k-meansunsupervised learning Google Predict MS Azure ML
- 12. Inês Almeida . PAPIs connect . March 2016 III. perfomance
- 13. Inês Almeida . PAPIs connect . March 2016 Amazon ML test set accuracy 81% 82% 81% 81% Google Predict MS Azure ML scikit learn
- 14. Inês Almeida . PAPIs connect . March 2016 what is the best ML solution for us?
- 15. Inês Almeida . PAPIs connect . March 2016 • distributed, large-scale solution _ Hadoop (HDFS) for data storage _ Spark for ML computing _ requires much effort
- 16. Inês Almeida . PAPIs connect . March 2016 • single-machine solution _ MongoDB for data storage _ Python packages for ML computing _ exploits our current architecture _ works fine for our scale
- 17. Inês Almeida . PAPIs connect . March 2016 Liquid data (MongoDB) models (MongoDB) data processing model training predicting API (Flask) ML Web Service (pandas, sklearn, theano, …)
- 18. Inês Almeida . PAPIs connect . March 2016 what is the best ML solution for you?
- 19. Inês Almeida . PAPIs connect . March 2016 • if using an external provider _ ML services need some data science knowledge _ keep data preprocessing on your side • if building your own solution _ exploit your product’s strengths _ start simple, then build on it
- 20. Inês Almeida . PAPIs connect . March 2016 alternatives • bigml _ generic ml service that uses random forests • prediction.io _ open source ML server with customizable templates • algorithmia _ algorithm marketplace (not just ML)
- 21. Inês Almeida . PAPIs connect . March 2016 resources Machine Learning as a Service on Liquid Blog https://blog.onliquid.com/machine-learning-service-benchmark/ Machine learning APIs: which performs best? by Louis Dorard http://www.louisdorard.com/blog/machine-learning-apis-comparison Principles of Machine Learning Benchmarking by Joey Richard http://www.wise.io/blog/principles-of-machine-learning-benchmarking Does off-the-shelf machine learning need a benchmark? by Jay Kreps http://blog.empathybox.com/post/18810157226/does-off-the-shelf-machine-learning-need-a