Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. ML for Energy Trading and Automotive SectorBigML, Inc
Machine Learning for Energy Trading, Automotive Sector, and Logistics, presented by BigML's Partners A1 Digital.
Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
Yuri is a Member of Technical Staff / Data Scientist at eBay in New York City. He is currently focused on developing scalable machine learning algorithms to produce high quality item recommendations. Yuri holds a Ph.D. degree from the Applied Physics and Applied Mathematics department from Columbia University and an undergraduate degree in Physics from UC Berkeley.
Abstract Summary:
Innovations in Recommender Systems for a Semi-structured Marketplace:
eBay has over 1 billion live items on the site at any given time. The lack of structured information about listings as well as variable inventory makes traditional collaborative filtering algorithms difficult to use in eBay’s large semi-structured marketplace. We will discuss approaches to overcome these challenges using machine learning and deep learning (both text and image based models). The details of the sampling strategy, feature engineering, and machine learned ranking model are all important for delivering improved operational metrics in A/B tests. We will cover both system architecture engineering as well as data science and machine learning methods that were developed to generate high quality recommendations.
Prediction of company bankruptcy. Learn about how Machine Learning finds insights of the Czech Business Landscape, presented by Lucie Beranová, Ph.D. Student at Prague University of Economics and Business (VSE) and Data Scientist at Vodafone.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Anatomy of an Application: Machine Learning End-to-End - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. ML for Energy Trading and Automotive SectorBigML, Inc
Machine Learning for Energy Trading, Automotive Sector, and Logistics, presented by BigML's Partners A1 Digital.
Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
Yuri is a Member of Technical Staff / Data Scientist at eBay in New York City. He is currently focused on developing scalable machine learning algorithms to produce high quality item recommendations. Yuri holds a Ph.D. degree from the Applied Physics and Applied Mathematics department from Columbia University and an undergraduate degree in Physics from UC Berkeley.
Abstract Summary:
Innovations in Recommender Systems for a Semi-structured Marketplace:
eBay has over 1 billion live items on the site at any given time. The lack of structured information about listings as well as variable inventory makes traditional collaborative filtering algorithms difficult to use in eBay’s large semi-structured marketplace. We will discuss approaches to overcome these challenges using machine learning and deep learning (both text and image based models). The details of the sampling strategy, feature engineering, and machine learned ranking model are all important for delivering improved operational metrics in A/B tests. We will cover both system architecture engineering as well as data science and machine learning methods that were developed to generate high quality recommendations.
Prediction of company bankruptcy. Learn about how Machine Learning finds insights of the Czech Business Landscape, presented by Lucie Beranová, Ph.D. Student at Prague University of Economics and Business (VSE) and Data Scientist at Vodafone.
*Machine Learning School for Business Schools 2021: Virtual Conference.
Anatomy of an Application: Machine Learning End-to-End - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Building Custom Machine Learning Algorithms with Apache SystemMLsparktc
This document discusses Apache SystemML, which is a machine learning framework for building custom machine learning algorithms on Apache Spark. It originated from research projects at IBM involving machine learning on Hadoop. SystemML aims to allow data scientists to build ML solutions using languages like R and Python, while executing algorithms on big data platforms like Spark. It provides a high-level language for expressing algorithms and performs automatic parallelization and optimization. The document demonstrates SystemML through a matrix factorization example for a targeted advertising problem. It shows how to use SystemML, Spark and Zeppelin together to build a custom algorithm and optimize part of the machine learning pipeline.
H2O World - Machine Learning at Comcast - Andrew Leamon & Chushi RenSri Ambati
1) Comcast uses machine learning for personalized recommendations on their X1 platform and to predict trending shows.
2) They apply machine learning to identify customer service issues that could potentially be avoided by a truck roll through predictive modeling.
3) Comcast also uses machine learning to develop a customer experience metric to help prioritize network deployments and understand customer needs.
BigML brings Principal Component Analysis (PCA) to the platform, a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. BigML PCA unique implementation is distinct from other approaches to PCA in that it can handle numeric and non-numeric data types, including text, categorical, items fields, as well as combinations of different data types. PCA can be used in any industry vertical as a preprocessing technique to improve supervised learning performance, with the caveat that some measure of interpretability may be sacrificed. It is commonly applied in fields with high dimensional data including bioinformatics, quantitative finance, and signal processing.
Transamerica developed a product recommender platform using big data and machine learning to increase customer satisfaction and cross-selling opportunities. The platform ingests customer data from various sources and uses Hadoop technologies like Hive, HBase, and Spark for storage, processing and predictive analytics. Models are built using H2O for tasks like binary classification to recommend burial insurance, term vs universal life insurance, and regression to predict policy face amounts and premiums. A proof of concept user interface was also developed to demonstrate personalized recommendations to customers based on their profile.
Machine Learning in Production with Dato Predictive ServicesTuri, Inc.
The document discusses Dato Predictive Services, a machine learning platform that helps deploy, serve, monitor, and manage machine learning models in production. It provides an overview of key capabilities like deploying models through different options, monitoring model performance and product usage, and evaluating models with online experiments. These capabilities aim to address common challenges of machine learning in production like deploying trained models, monitoring their behavior, and continuously improving them. The presentation includes a demo of a book recommender application built with Dato Predictive Services.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Planning a data solution - "By Failing to prepare, you are preparing to fail"Itai Yaffe
Eynav Mass (VP R&D) @ Oribi:
When it comes to data solutions, one-size doesn't fit all.
Choosing the right best-matching database, or data tools, can be a game-changer for your system.
How can you take such a decision effectively?
The system, the company, the product, and probably your team - all are evolving, and the best solution for today may not fit tomorrow's needs.
In order to pick a data solution for longer term, you should evaluate the optional data tools according to several factors.
These factors will reflect the requirements looking forward.
At the session, we will discuss these factors, along with sharing some real-life stories and lessons learned, to help you properly plan & prepare your data solutions.
This document discusses recommendations and personalization techniques used at Rakuten. It describes the challenges of recommendations including different languages, user behaviors, and business areas. It provides an overview of recommendation systems and discusses approaches like collaborative filtering using user-user or item-item similarities, and matrix factorization. The document also discusses how to generate recommendations from unary data using co-occurrence analysis and similarity metrics.
MLSEV. Logistic Regression, Deepnets, and Time Series BigML, Inc
Supervised Learning (Part II): Logistic Regression, Deepnets, and Time Series, by BigML.
MLSEV 2019: 1st edition of the Machine Learning School in Seville, Spain.
TensorFlow London 18: Dr Alastair Moore, Towards the use of Graphical Models ...Seldon
This document discusses using graphical models and machine learning techniques to improve management processes for 21st century businesses. It argues that current management practices have not evolved significantly and are poorly integrated with digital systems. The document proposes designing management tools and business models based on principles of continuous learning and integration between human and machine systems. It presents examples like the machine learning canvas and Wardley mapping to help conceptualize business problems and solutions in a way that facilitates machine learning. The goal is to develop tools that allow businesses to constantly adapt and improve using data and predictive analytics.
Quick iteration and reusability of metric calculations for powerful data exploration.
At Looker, we want to make it easier for data analysts to service the needs of the data-hungry users in their organizations. We believe too much of their time is spent responding to ad hoc data requests and not enough time is spent building, experimenting, and embellishing a robust model of the business. Worse yet, business users are starving for data, but are forced to make important decisions without access to data that could guide them in the right direction. Looker addresses both of these problems with a YAML-based modeling language called LookML.
This paper walks through a number of data modeling examples, demonstrating how to use LookML to generate, alter, and update reports—without the need to rewrite any SQL. With LookML, you build your business logic, defining your important metrics once and then reusing them throughout a model—allowing quick, rapid iteration of data exploration, while also ensuring the accuracy of the SQL that’s generated. Small updates are quick and can be made immediately available to business users to manipulate, iterate, and transform in any way they see fit.
How to Build a Successful Data Team - Florian Douetteau @ PAPIs ConnectPAPIs.io
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.
Webinar - Fraud Detection - Palombo (20160428)Turi, Inc.
The document outlines a webinar presented by Alon Palombo of Dato on fraud detection. The webinar agenda includes an introduction of Dato, an overview of the data science workflow and what constitutes fraud, a live demo of fraud detection using real data, and time for questions. Various techniques for fraud detection are discussed, including classification, graph analytics, time series analysis, and anomaly detection.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
H2O World - Machine Learning for non-data scientistsSri Ambati
The document discusses how businesses can leverage data and machine learning to make better decisions through asking the right questions, defining problems, analyzing data, and bridging communication gaps between data scientists and decision makers. It provides examples of how different machine learning techniques like supervised learning, unsupervised learning, classification, regression, deep learning, and clustering can be applied to common business problems. The document also outlines the overall business decision process and roles of data scientists.
Martin Stein, G5 - Driving Marketing Performance with H2O Driverless AI - H2O...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/f4b2Yoe9JEs
Combining H2O Driverless AI, H2O-3, and AWS for developing and deploying AI solutions on scale.
Martin Stein is a seasoned Product and Marketing executive with a successful track record delivering large-scale advanced analytics and marketing analytics services and products. Martin has served as Board Member, C-Level Executive and subject matter expert in a variety of industries (Marketing, Finance, Real Estate and Media). Currently, Martin as Chief Analytics Officer for g5, a leader in real estate marketing optimization. G5 is a predictive marketing SaaS company that uses AI and other emerging technologies to help marketers amplify their impact.
Scaling & Managing Production Deployments with H2O ModelOpsSri Ambati
This presentation was made on June 30th, 2020.
Recording of the presentation is available here: https://youtu.be/9LajqAL_CU8
As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary.
H2O ModelOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments.
Speaker's Bio:
Felix is a part of the Customer Success team in Asia Pacific at H2O.ai. An engineer and an IIM alumni, Felix has held prominent positions in the data science industry.
Machine Learning system architecture – Microsoft Translator, a Case Study : ...Vishal Chowdhary
Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
Jaichander has over 5 years of experience in manual testing and data warehousing testing. He has expertise in SQL, databases like Oracle and MySQL, ETL tools like Informatica PowerCenter, and BI tools like Cognos. He has worked on projects involving data extraction, transformation and loading from source systems to data marts and warehouses, with reporting on the transformed data. His responsibilities have included test case design, execution, defect logging and tracking, understanding requirements and designs, and mentoring other team members.
Desjardins Group Leverages CA Workload Automation as It Begins Its DevOps Jou...CA Technologies
DevOps focuses on delivering more application innovation to the market in smaller releases and at a faster cadence. Join us as Desjardins Group and CA talk about how they are planning to leverage DevOps and CA Automation to increase their speed of execution on evolving critical business applications. Understand how building the right organization and processes play a large part in their success along with the great tools that are taking them into the future.
For more information, please visit http://cainc.to/Nv2VOe
Building Custom Machine Learning Algorithms with Apache SystemMLsparktc
This document discusses Apache SystemML, which is a machine learning framework for building custom machine learning algorithms on Apache Spark. It originated from research projects at IBM involving machine learning on Hadoop. SystemML aims to allow data scientists to build ML solutions using languages like R and Python, while executing algorithms on big data platforms like Spark. It provides a high-level language for expressing algorithms and performs automatic parallelization and optimization. The document demonstrates SystemML through a matrix factorization example for a targeted advertising problem. It shows how to use SystemML, Spark and Zeppelin together to build a custom algorithm and optimize part of the machine learning pipeline.
H2O World - Machine Learning at Comcast - Andrew Leamon & Chushi RenSri Ambati
1) Comcast uses machine learning for personalized recommendations on their X1 platform and to predict trending shows.
2) They apply machine learning to identify customer service issues that could potentially be avoided by a truck roll through predictive modeling.
3) Comcast also uses machine learning to develop a customer experience metric to help prioritize network deployments and understand customer needs.
BigML brings Principal Component Analysis (PCA) to the platform, a key unsupervised Machine Learning technique used to transform a given dataset in order to yield uncorrelated features and reduce dimensionality. BigML PCA unique implementation is distinct from other approaches to PCA in that it can handle numeric and non-numeric data types, including text, categorical, items fields, as well as combinations of different data types. PCA can be used in any industry vertical as a preprocessing technique to improve supervised learning performance, with the caveat that some measure of interpretability may be sacrificed. It is commonly applied in fields with high dimensional data including bioinformatics, quantitative finance, and signal processing.
Transamerica developed a product recommender platform using big data and machine learning to increase customer satisfaction and cross-selling opportunities. The platform ingests customer data from various sources and uses Hadoop technologies like Hive, HBase, and Spark for storage, processing and predictive analytics. Models are built using H2O for tasks like binary classification to recommend burial insurance, term vs universal life insurance, and regression to predict policy face amounts and premiums. A proof of concept user interface was also developed to demonstrate personalized recommendations to customers based on their profile.
Machine Learning in Production with Dato Predictive ServicesTuri, Inc.
The document discusses Dato Predictive Services, a machine learning platform that helps deploy, serve, monitor, and manage machine learning models in production. It provides an overview of key capabilities like deploying models through different options, monitoring model performance and product usage, and evaluating models with online experiments. These capabilities aim to address common challenges of machine learning in production like deploying trained models, monitoring their behavior, and continuously improving them. The presentation includes a demo of a book recommender application built with Dato Predictive Services.
Recent Gartner and Capgemini studies predict only around 25% of data science projects are successful and only around 15% make it to full-scale production. Of these, many degrade in performance and produce disappointing results within months of implementation. How can focusing on the desired business outcomes and business use cases throughout a data science project help overcome the odds?
Planning a data solution - "By Failing to prepare, you are preparing to fail"Itai Yaffe
Eynav Mass (VP R&D) @ Oribi:
When it comes to data solutions, one-size doesn't fit all.
Choosing the right best-matching database, or data tools, can be a game-changer for your system.
How can you take such a decision effectively?
The system, the company, the product, and probably your team - all are evolving, and the best solution for today may not fit tomorrow's needs.
In order to pick a data solution for longer term, you should evaluate the optional data tools according to several factors.
These factors will reflect the requirements looking forward.
At the session, we will discuss these factors, along with sharing some real-life stories and lessons learned, to help you properly plan & prepare your data solutions.
This document discusses recommendations and personalization techniques used at Rakuten. It describes the challenges of recommendations including different languages, user behaviors, and business areas. It provides an overview of recommendation systems and discusses approaches like collaborative filtering using user-user or item-item similarities, and matrix factorization. The document also discusses how to generate recommendations from unary data using co-occurrence analysis and similarity metrics.
MLSEV. Logistic Regression, Deepnets, and Time Series BigML, Inc
Supervised Learning (Part II): Logistic Regression, Deepnets, and Time Series, by BigML.
MLSEV 2019: 1st edition of the Machine Learning School in Seville, Spain.
TensorFlow London 18: Dr Alastair Moore, Towards the use of Graphical Models ...Seldon
This document discusses using graphical models and machine learning techniques to improve management processes for 21st century businesses. It argues that current management practices have not evolved significantly and are poorly integrated with digital systems. The document proposes designing management tools and business models based on principles of continuous learning and integration between human and machine systems. It presents examples like the machine learning canvas and Wardley mapping to help conceptualize business problems and solutions in a way that facilitates machine learning. The goal is to develop tools that allow businesses to constantly adapt and improve using data and predictive analytics.
Quick iteration and reusability of metric calculations for powerful data exploration.
At Looker, we want to make it easier for data analysts to service the needs of the data-hungry users in their organizations. We believe too much of their time is spent responding to ad hoc data requests and not enough time is spent building, experimenting, and embellishing a robust model of the business. Worse yet, business users are starving for data, but are forced to make important decisions without access to data that could guide them in the right direction. Looker addresses both of these problems with a YAML-based modeling language called LookML.
This paper walks through a number of data modeling examples, demonstrating how to use LookML to generate, alter, and update reports—without the need to rewrite any SQL. With LookML, you build your business logic, defining your important metrics once and then reusing them throughout a model—allowing quick, rapid iteration of data exploration, while also ensuring the accuracy of the SQL that’s generated. Small updates are quick and can be made immediately available to business users to manipulate, iterate, and transform in any way they see fit.
How to Build a Successful Data Team - Florian Douetteau @ PAPIs ConnectPAPIs.io
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.
Webinar - Fraud Detection - Palombo (20160428)Turi, Inc.
The document outlines a webinar presented by Alon Palombo of Dato on fraud detection. The webinar agenda includes an introduction of Dato, an overview of the data science workflow and what constitutes fraud, a live demo of fraud detection using real data, and time for questions. Various techniques for fraud detection are discussed, including classification, graph analytics, time series analysis, and anomaly detection.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
H2O World - Machine Learning for non-data scientistsSri Ambati
The document discusses how businesses can leverage data and machine learning to make better decisions through asking the right questions, defining problems, analyzing data, and bridging communication gaps between data scientists and decision makers. It provides examples of how different machine learning techniques like supervised learning, unsupervised learning, classification, regression, deep learning, and clustering can be applied to common business problems. The document also outlines the overall business decision process and roles of data scientists.
Martin Stein, G5 - Driving Marketing Performance with H2O Driverless AI - H2O...Sri Ambati
This session was recorded in San Francisco on February 5th, 2019 and can be viewed here: https://youtu.be/f4b2Yoe9JEs
Combining H2O Driverless AI, H2O-3, and AWS for developing and deploying AI solutions on scale.
Martin Stein is a seasoned Product and Marketing executive with a successful track record delivering large-scale advanced analytics and marketing analytics services and products. Martin has served as Board Member, C-Level Executive and subject matter expert in a variety of industries (Marketing, Finance, Real Estate and Media). Currently, Martin as Chief Analytics Officer for g5, a leader in real estate marketing optimization. G5 is a predictive marketing SaaS company that uses AI and other emerging technologies to help marketers amplify their impact.
Scaling & Managing Production Deployments with H2O ModelOpsSri Ambati
This presentation was made on June 30th, 2020.
Recording of the presentation is available here: https://youtu.be/9LajqAL_CU8
As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary.
H2O ModelOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments.
Speaker's Bio:
Felix is a part of the Customer Success team in Asia Pacific at H2O.ai. An engineer and an IIM alumni, Felix has held prominent positions in the data science industry.
Machine Learning system architecture – Microsoft Translator, a Case Study : ...Vishal Chowdhary
Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
Jaichander has over 5 years of experience in manual testing and data warehousing testing. He has expertise in SQL, databases like Oracle and MySQL, ETL tools like Informatica PowerCenter, and BI tools like Cognos. He has worked on projects involving data extraction, transformation and loading from source systems to data marts and warehouses, with reporting on the transformed data. His responsibilities have included test case design, execution, defect logging and tracking, understanding requirements and designs, and mentoring other team members.
Desjardins Group Leverages CA Workload Automation as It Begins Its DevOps Jou...CA Technologies
DevOps focuses on delivering more application innovation to the market in smaller releases and at a faster cadence. Join us as Desjardins Group and CA talk about how they are planning to leverage DevOps and CA Automation to increase their speed of execution on evolving critical business applications. Understand how building the right organization and processes play a large part in their success along with the great tools that are taking them into the future.
For more information, please visit http://cainc.to/Nv2VOe
Shuchi Agrawal has over 7 years of experience working as a project leader and technical lead on various projects. They have expertise in technologies such as Teradata, SQL, PL/SQL, Netezza, and Unix. Some of the key projects they worked on include database remodeling for Walgreens, Teradata upgrades, apparel transformation for ToysRUs, and generating weekly reports for Nielsen Online. They have a degree in electrical engineering and certifications in project management and Netezza.
Yeswanth Kumar has over 3 years of experience working with Teradata, Oracle, and Unix technologies. He has a strong skillset in relational database management systems, data warehousing, ETL processes, and data analytics. His most recent role involved developing dashboards and providing operations support for Nordstrom, a major US fashion retailer, where he was responsible for requirements gathering, designing queries, transforming data, and ensuring system performance. He has experience in agile and DevOps projects and aims to be a versatile team player.
This document provides information about Badre Maktari's work experience and qualifications. It summarizes his 13+ years of experience managing projects in areas like operations, quality, and IT. It also includes recommendations from past managers that praise Maktari's skills, creativity, commitment to quality, and his strong background and experience. The document aims to showcase Maktari's skills and qualifications for an innovation or business intelligence project manager role.
Rajnish Kumar has over 7 years of experience in the IT industry as a Team Lead and Senior Software Engineer. He has extensive experience with ETL tools like DataStage and databases like Teradata, Oracle, and DB2. Some of his project experiences include developing ETL processes and data warehouses for clients in telecom and banking industries. He has technical skills in areas like data warehousing, ETL, SQL, shell scripting, and UNIX.
How To Rearchitecting Legacy System
Meetup By Software Architect Indonesia Community https://www.meetup.com/Software-Architect-Indonesia/events/246479075/?_cookie-check=DrXW508tQr2LKtBD
Speaker: Moch Nasrullah Rahmani
BBM Engineer
Take care of the people, the products,
and the profits: in that order.
The document is Amith Mansingh Ramanund's resume, which details his contact information, personal details, preferred job positions, work experience including roles as a Business Analyst at DHL Global Forwarding and Accenture, skills including SQL, PL/SQL, Tableau, and projects he has worked on such as standardizing tracking, claims, and distribution systems and developing BI reports in Tableau and SSRS.
Brainstack Technology is a service based tech start which offers great services in the field of DevOps,Cloud services,machine learning,IoT and software testing.
We have partnered with start-ups,government and telecom companies to deliver some great solutions.
Our aim is to deliver the complete range of technology services starting from ideation to execution, thus enabling our global clients to outperform the competition.
The candidate has over 5 years of experience as a Software Engineer working on various projects for Tesco. Their roles have included designing, developing, testing and deploying SSIS packages and interfaces to integrate different retail systems. They have strong skills in SQL Server, SSIS and relational databases. Their most recent projects involve testing applications like Checkout Back Office and implementing new functionalities around areas such as contactless payments and encryption.
This document discusses intelligent document processing solutions and how they can help organizations overcome challenges with unstructured and diverse documents. It introduces a predictive artificial intelligence approach that can handle unprecedented document variation with high accuracy. Examples are given of how one insurance company was able to automate document processing, reduce costs by $3 million, and improve turnaround times from 48 hours to 8 hours by implementing this solution. The key advantage highlighted is its ability to learn from data and continuously improve over time.
The data that your business collects is constantly growing, making it increasingly difficult for traditional systems to keep up with resource demands. Understanding your big data can help you serve your customers better, improve product quality, and grow your revenue, but you need a platform that can handle the strain.
In hands-on tests in our datacenter, the Scalable Modular Server DX2000 from NEC processed big data quickly and scaled nearly linearly as we added server nodes. In our k-means data cluster analysis test, a DX2000 solution running Apache Spark and Red Hat Enterprise Linux OpenStack Platform processed 100GB in approximately 2 minutes. We also saw that as we doubled the number of server nodes, the DX2000 solution cut analysis time in half when processing the same amount of data, producing excellent scalability.
The Scalable Modular Server DX2000 by NEC is a good choice when you’re ready to put big data to work for you.
The DevOps journey in an Enterprise - Continuous Lifecycle London 2016Anders Lundsgård
Presentation about the DevOps movement at Scania. Conference: Continuous Lifecycle London 2016-05-03. http://continuouslifecycle.london/
By Anders Lundsgård (@anderslundsgard) and Mattias Järnhäll (@mattiasjarnhall)
This document is a resume for Guru Prasad H G summarizing his career experience and qualifications. He has over 7 years of experience in software testing for various projects in the telecom and retail industries. His experience includes testing billing systems, preparing test plans and reports, and working on projects for clients like IKEA, T-Mobile UK, Kraft, and British Telecom. He is currently working as a consultant for Capgemini and has experience in both manual and automation testing methods.
BOSS Technologies is an Oracle Gold Partner offering Solutions for Staffing and Services. Let us build your Oracle Implementation Team. Scalable. Enterprise. Services.
Praveen Kumar Kannan has over 7 years of experience in retail domains such as e-commerce order management and business intelligence. He has expertise in big data technologies like Apache Hadoop, Hive, Pig and MapReduce. He has worked on projects for clients such as Target Corporation and Marks & Spencer, designing and implementing solutions for business intelligence, e-commerce and order fulfillment. Currently he works as a module lead for a project processing data from Target.com on the Hadoop stack.
Kier Group was looking to manage massive data growth from business expansion. They implemented a software-defined storage solution from Tectrade to provide a flexible architecture. This transition saved Kier £170,000 over three years through reduced maintenance costs and hardware investments while doubling performance. Regular reviews between Tectrade and Kier provide ongoing clarity on Kier's storage environment and future needs.
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
The document discusses building an anomaly detector model to identify unusual transactions in a dataset. It describes loading transaction data with 31 features into the BigML platform and creating an anomaly detector model. The model scores new data and identifies the most anomalous fields to help detect fraud. Creating the anomaly detector involves interpreting the data, exploring the dataset distribution, and setting a threshold score to define what is considered anomalous.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
This document describes a proposed solution using machine learning and artificial intelligence to help create a safer stadium experience. The solution involves two parts: 1) linking access to stadiums to a verified identity through a fan app for preregistration, and 2) using AI/ML to help detect unwanted behaviors or events early. The rest of the document provides more details on the proposed smart video review framework, including using computer vision and audio analysis techniques to help identify issues like flares, flags, banners, chants including monkey chants. The goal is to help reviewers more efficiently identify potential problems but with privacy, ethics and human oversight.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
The document discusses the need for citizen developers and humans in the AI/ML process. It notes that while technology and talent are important, company culture must also support broad data analytics and AI/ML adoption. It then provides examples of how involving domain experts can help attribute meaning to correlations and build better causal models to improve AI systems. The document advocates for a systems thinking approach and having humans in the loop to help AI/ML systems consider the wider context and avoid issues like bias.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
2. Company Creation
Technology 2 Client (T2C) was
founded in December 2003 and
started operating in January 2004
establishing the first office in the
centre of Barcelona.
2003
New HQ
In 2014, T2C moves to Avda.
Diagonal, ‘prime’ zone for tech
companies in Barcelona
consolidating employees in a
single office.
2014
México Subsidiary
Where we expect to develop and
provide our Advanced Analytics,
development and SAP services.
2019
Málaga Office
Through an agreement with the
Tech hub in Málaga (PTA) and
the Málaga University (UMA), we
facilitate our entrance in the hub.
2018
3. Our Goals
We are a company built by people that
aims to help people. We care less about
numbers than we do about building trust.
TALENT
DNA
We help our clients to improve their processes and
consolidate projects using the best talent available.
CONSISTENCY
15 years
Since our creation we have never stopped growing
in a organic manner that allows us to maintain the
core values of the company.
INNOVATION
Future
We are always looking to anticipate what’s next by
constantly scanning the market looking for new
products and trends.
EFICIENCY
Maximum
Obsessed with efficiency, we thrive in projects with
maximum impact and added value.
5. T2C
Global logistics market is anticipated to register a CAGR of 3.48% from
2016 to 2022 to attain a market size of around $12,256 billion by 2022.
Allied Market Research
12. T2C
Project Definition
QUESTION
Define the question we are going to answer.
“Is an expedition likely to fail?”
LABEL
Define which label we are going to predict (Supervised Learning).
“Expedition outcome: Binary classification SUCCESS/FAILURE”
AMOUNT
Is there enough data to answer that question? Are there enough positive instances?
“Do our expeditions fail that often? Do we properly record these failures?”
GOALS
Define precisely what a training instance is, the goal and the evaluation method.
“An expedition can contain several movements.”
“ Improve service level.”
“We will measure the ROI of the project based on average failures reduction.”
20. T2C
2. Data Cleaning
3. Data Transformation
Transformations need to be applied to raw data to obtain Machine Learning ready data.
• Types of missing values:
• Meaningful missing: The fact that a value is missing adds information.
• Meaningless missing: The fact that a value is missing is accidental.
• Strategies:
• Drop rows with missing values if there is a small percentage.
• Drop features with a high percentage of missing values.
• Impute missing values with static content: median, mode, mean…
• Use Machine Learning techniques to impute missing values.
22. T2C
Selected Features
Feature Description
date Date of order generation.
Planned_delivery_date Planned delivery date of the order.
Final_Destination Arrival destination code.
destination_name Destination name.
destination_location Destination location.
destination_zip_code Destination zip code.
destination_province Destination province.
destination_region Destination province.
origin Origin code.
origin_name Name of the origin of the order.
num_stops Number of stops made by the driver.
diff_hours Number of hours between order generation and estimated
time of delivery.
Feature Description
distance Distance (in a straight line) between origin and destination.
num_pallets Number of pallets in the shipment.
num_volumes Number of boxes in the shipment.
weight weight (kg).
urgent Indicates if a shipment is urgent.
is_workingday_delivery Indicates if it is a workday the day of delivery.
is_workingday_delivery_d+1 Indicates if it is a workday the day after of delivery.
is_workingday_delivery_d+2 Indicates if it is a workday two day after of delivery.
is_workingday_delivery_d-1 Indicates if it is a workday the previous day of delivery.
is_workingday_delivery_d-2 Indicates if it is a workday two days previous day of delivery.
order_ok OBJECTIVE FIELD: Order completed with or without success.