Brief presentation that lays out the landscape and current roadmap for automatic or guided semi-automatic #machinelearning in H2O. Tags: #automl #datascience #bigdata #analytics #ai
https://www.youtube.com/watch?v=ZZSe3osXK_E
Slides for a talk I gave in early summer 2016 introducing #machinelearning #bigdata #analytics, and why @h2oai is the best choice for most predictive analytics needs.
Predicting Patient Outcomes in Real-Time at HCASri Ambati
Data Scientist Allison Baker and Development Manager of Data Products Cody Hall work with a talented team of data scientists, software engineers, and web developers, and are building the framework and infrastructure to support a real-time prediction application, with the ability to scale across the entire company. Paramount to these efforts has been the capability of integrating the architecture for software production with the predictive models generated by H2O. This talk will review the processes by which HCA is building a pipeline to predict patient outcomes in real-time, heavily relying on H2O’s POJO scoring API and implemented in Clojure data processing. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Mortal analytics - Covid-19 and the problem of data qualityLars Albertsson
Social media are full of Covid-19 graphs, each pointing to an "obvious" conclusion that fits the author's agenda. Unfortunately, even the official sources publish analytics that point at incorrect conclusions. Bad data quality has become a matter of life and death.
We look at the quality problems with official Covid-19 data presentations. The problems are common in all domains, and solutions are known, but not widespread. We describe tools and patterns that data mature companies use to assess and improve data quality in similar situations. Mastering data quality and data operations is a prerequisite for building sustainable AI solutions, and we will explain how these patterns fit into machine learning product development.
The presentation was given at the SOCM'16 workshop at the WWW16 conference. It corresponds to the research study titled "Observlets: Empowering Analytical Observations on Web Observatory".
New times, new hype. Buzzwords like big data and Hadoop have been changed to AI and machine learning. But it's not technology, old or new, nor machine learning that separates companies that get value from data from the companies that struggle .
When big data was at its peak, several young, technology-intensive companies succeeded in absorbing big data successfully. They acquired large Hadoop clusters, learned to master data and created valuable products with machine learning. However, big data has had a limited impact at traditional companies, and the list of long and expensive data lake and Hadoop projects is long.
The key to implementing successful projects that transform data into business value is to democratise data - making it accessible and easy to use within an organisation.
Monitorama: How monitoring can improve the rest of the companyJeff Weinstein
Monitoring can improve the entire company by sharing data and techniques across teams. By implementing structured logging, automatic metrics collection, and common data visualization tools, monitoring can become the central data platform. This allows all teams like developers, analysts, and executives to access insights that help improve products, prioritize issues, and make data-driven decisions.
Presentation held during the NKUA postgraduate course “DATA BASES MANAGEMENT SYSTEMS” on 6th of December 2016 at National and Kapodistrian University of Athens.
Nikolas Laskaris, UoA
Giota Koltsida, UoA
http://bit.ly/2hEkn3G
Privacy and personal integrity has become a focus topic, due to the upcoming GDPR deadline in May 2018. GDPR puts limits on data storage, retention, and access, and also give users rights to have their data deleted and get information about the data stored. This constraints technical solutions, and makes it challenging to build systems that efficiently make use of sensitive data. This talk provides an engineering perspective on privacy. We highlight pitfalls and topics that require early attention. We describe technical patterns for complying with the "right to be forgotten" without sacrificing the ability to use data for product features. The content of the talk is based on real world experience from handling privacy protection in large scale data processing environments.
Slides for a talk I gave in early summer 2016 introducing #machinelearning #bigdata #analytics, and why @h2oai is the best choice for most predictive analytics needs.
Predicting Patient Outcomes in Real-Time at HCASri Ambati
Data Scientist Allison Baker and Development Manager of Data Products Cody Hall work with a talented team of data scientists, software engineers, and web developers, and are building the framework and infrastructure to support a real-time prediction application, with the ability to scale across the entire company. Paramount to these efforts has been the capability of integrating the architecture for software production with the predictive models generated by H2O. This talk will review the processes by which HCA is building a pipeline to predict patient outcomes in real-time, heavily relying on H2O’s POJO scoring API and implemented in Clojure data processing. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Mortal analytics - Covid-19 and the problem of data qualityLars Albertsson
Social media are full of Covid-19 graphs, each pointing to an "obvious" conclusion that fits the author's agenda. Unfortunately, even the official sources publish analytics that point at incorrect conclusions. Bad data quality has become a matter of life and death.
We look at the quality problems with official Covid-19 data presentations. The problems are common in all domains, and solutions are known, but not widespread. We describe tools and patterns that data mature companies use to assess and improve data quality in similar situations. Mastering data quality and data operations is a prerequisite for building sustainable AI solutions, and we will explain how these patterns fit into machine learning product development.
The presentation was given at the SOCM'16 workshop at the WWW16 conference. It corresponds to the research study titled "Observlets: Empowering Analytical Observations on Web Observatory".
New times, new hype. Buzzwords like big data and Hadoop have been changed to AI and machine learning. But it's not technology, old or new, nor machine learning that separates companies that get value from data from the companies that struggle .
When big data was at its peak, several young, technology-intensive companies succeeded in absorbing big data successfully. They acquired large Hadoop clusters, learned to master data and created valuable products with machine learning. However, big data has had a limited impact at traditional companies, and the list of long and expensive data lake and Hadoop projects is long.
The key to implementing successful projects that transform data into business value is to democratise data - making it accessible and easy to use within an organisation.
Monitorama: How monitoring can improve the rest of the companyJeff Weinstein
Monitoring can improve the entire company by sharing data and techniques across teams. By implementing structured logging, automatic metrics collection, and common data visualization tools, monitoring can become the central data platform. This allows all teams like developers, analysts, and executives to access insights that help improve products, prioritize issues, and make data-driven decisions.
Presentation held during the NKUA postgraduate course “DATA BASES MANAGEMENT SYSTEMS” on 6th of December 2016 at National and Kapodistrian University of Athens.
Nikolas Laskaris, UoA
Giota Koltsida, UoA
http://bit.ly/2hEkn3G
Privacy and personal integrity has become a focus topic, due to the upcoming GDPR deadline in May 2018. GDPR puts limits on data storage, retention, and access, and also give users rights to have their data deleted and get information about the data stored. This constraints technical solutions, and makes it challenging to build systems that efficiently make use of sensitive data. This talk provides an engineering perspective on privacy. We highlight pitfalls and topics that require early attention. We describe technical patterns for complying with the "right to be forgotten" without sacrificing the ability to use data for product features. The content of the talk is based on real world experience from handling privacy protection in large scale data processing environments.
400 million Search Results -Predict Contextual Ad Clicks Sri Ambati
H2O.ai is an open source machine learning platform used for predictive analytics and data science. The document discusses H2O.ai's products and algorithms for machine learning tasks like click prediction. It provides an overview of the company, executives and advisors, and the platform's features like its open source APIs for R and Python, Spark integration, and cutting-edge machine learning algorithms like deep learning and gradient boosted machines. An example use case is presented on using H2O.ai to build a click prediction model from a Kaggle competition dataset.
Robsonalves fotografia Fine Art 2016-2Robson Alves
José Diniz é um fotógrafo brasileiro nascido em Niterói que mora no Rio de Janeiro. Ele publicou vários livros de fotografia e teve seu trabalho exposto em diversos museus e galerias no Brasil, Argentina, Uruguai, Estados Unidos, França e Holanda. Diniz já recebeu vários prêmios e suas fotografias fazem parte de coleções de importantes instituições culturais.
Alice Lindorfer é uma fotógrafa de 22 anos que se formou em Fotografia em 2015 e tem seu próprio estúdio há um ano e meio. Ela sempre se identificou com a fotografia como forma de comunicação através de sentimentos e gosta de criar vínculos com os clientes fotografando as diferentes fases de suas vidas, incluindo newborns desde seu primeiro ensaio gratuito há três anos.
This document provides an introduction to using the Deducer graphical user interface (GUI) for R. It discusses loading and installing the Deducer and DeducerSurvival packages, exploring a sample dataset using the GUI's data viewer and generating frequency tables and graphs. Instructions are given on reading Excel data, converting variables to factors, and using the GUI to summarize categorical and continuous variables and add smoothing to graphs.
This document provides an overview and introduction to data mining using R and Rattle. It discusses data mining concepts and applications. It then introduces R as a programming language for statistical analysis and data mining. Rattle is presented as a graphical user interface tool built on R to make data mining more accessible. The document walks through installing and using Rattle to explore, visualize, model and evaluate data. It also discusses resources for learning more about R, Rattle and data mining.
This document provides instructions for installing R and R-Studio, two programs for performing advanced data analytics. It explains that R can be downloaded from its website for Linux, MacOS, and Windows, while R-Studio can also be downloaded from its website for those operating systems. The document then demonstrates how to use R-Studio, which displays the workspace, console, and ability to show graphics and other information across multiple tabs. It includes example R commands to help orient users and demonstrates assigning a value to a variable to show mastery of the basics.
Automating Machine Learning - Is it feasible?Manuel Martín
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
This document compares two integrated development environments (IDEs) for the R programming language: R-Studio and Rcmdr. R-Studio is a more powerful and flexible IDE that provides direct access to R code and facilitates interactions with R through its graphical interface. Rcmdr is simpler and more user-friendly, focusing on statistical analysis through buttons and menus. Both allow viewing data, but neither support data editing. The document provides guidelines for choosing between them and notes additional R IDEs under development.
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demandsMongoDB
The document provides 5 client scenarios where MongoDB was leveraged to solve data and architecture challenges. Each scenario describes the client, problem to be solved, and how MongoDB was used. Key features highlighted across scenarios included MongoDB's schema-less design, high performance, data residency controls via sharding, flexible data models, and transaction support which enabled solutions for event streaming, machine learning, microservices architecture, and handling historical insurance data.
This document discusses key considerations for implementing a Laboratory Information Management System (LIMS). It begins by explaining how LIMS can help manage larger volumes of data and samples compared to tools like Excel. The document then outlines common stages of LIMS implementation from basic databases to more advanced custom systems. Key functionality that LIMS can provide is explored, such as sample tracking, integration with other systems, and security management. Important factors for a successful LIMS implementation are also summarized, such as defining business needs, customization costs, and user training.
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...Databricks
I will share the vision and the production journey of how we build enterprise shared AI As A Service platforms with distributed deep learning technologies. Including those topics:
1) The vision of Enterprise Shared AI As A Service and typical AI services use cases at FinTech industry
2) The high level architecture design principles for AI As A Service
3) The technical evaluation journey to choose an enterprise deep learning framework with comparisons, such as why we choose Deep learning framework based on Spark ecosystem
4) Share some production AI use cases, such as how we implemented new Users-Items Propensity Models with deep learning algorithms with Spark,improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking etc.
5) Share some experiences and tips of using deep learning technologies on top of Spark , such as how we conduct Intel BigDL into a real production.
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses DevOps and MLOps practices for machine learning models. It outlines that while ML development shares some similarities with traditional software development, such as using version control and CI/CD pipelines, there are also key differences related to data, tools, and people. Specifically, ML requires additional focus on exploratory data analysis, feature engineering, and specialized infrastructure for training and deploying models. The document provides an overview of how one company structures their ML team and processes.
MongoDB World 2019: High Performance Auditing of Changes Based on MongoDB Cha...MongoDB
Take advantage of the elasticity of the cloud by creating resources that can heal themselves. Learn to create Compute Engine resources in GCP using Terraform that will install and configure a MongoDB replica set for you.
Comcast Labs Connect - PHLAI Conference Philadelphia 2018 Open Data Group
Open Data Group's Product Manager Rehgan Avon Speaks about Achieving AI and ML Operational Excellence at the Comcast Labs Connect - Artificial Intelligence And Machine Learning Conference in Philadelphia 2018.
Machine Learning - Eine Challenge für ArchitektenHarald Erb
Aufgrund vielfältiger potenzieller Geschäftschancen, die Machine Learning bietet, starten viele Unternehmen Initiativen für datengetriebene Innovationen. Dabei gründen sie Analytics-Teams, schreiben neue Stellen für Data Scientists aus, bauen intern Know-how auf und fordern von der IT-Organisation eine Infrastruktur für "heavy" Data Engineering & Processing samt Bereitstellung einer Analytics-Toolbox ein. Für IT-Architekten warten hier spannende Herausforderungen, u.a. bei der Zusammenarbeit mit interdisziplinären Teams, deren Mitglieder unterschiedlich ausgeprägte Kenntnisse im Bereich Machine Learning (ML) und Bedarfe bei der Tool-Unterstützung haben.
Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. Here we present BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated.
Techniques for scaling application with security and visibility in cloudAkshay Mathur
Akshay Mathur gives a presentation on techniques for scaling applications with security and visibility in the cloud. He discusses 8 growth phases applications typically go through including load balancing, gaining insights, content optimization, offloading services, content switching, preventing bot traffic and DDoS attacks, continuous delivery, and the need for a unified cloud application front end solution to manage these phases. He introduces Appcito CAFE as a service that provides capabilities across availability, performance, security and DevOps to simplify application scaling in the cloud.
Story of Algolytic. Assisting companies with lots of data to get insights and predictions about customer behaviour, predicting risks based on statistical data analysis.
Story of how we can help, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
Story of our company - what we offer, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools etc.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
400 million Search Results -Predict Contextual Ad Clicks Sri Ambati
H2O.ai is an open source machine learning platform used for predictive analytics and data science. The document discusses H2O.ai's products and algorithms for machine learning tasks like click prediction. It provides an overview of the company, executives and advisors, and the platform's features like its open source APIs for R and Python, Spark integration, and cutting-edge machine learning algorithms like deep learning and gradient boosted machines. An example use case is presented on using H2O.ai to build a click prediction model from a Kaggle competition dataset.
Robsonalves fotografia Fine Art 2016-2Robson Alves
José Diniz é um fotógrafo brasileiro nascido em Niterói que mora no Rio de Janeiro. Ele publicou vários livros de fotografia e teve seu trabalho exposto em diversos museus e galerias no Brasil, Argentina, Uruguai, Estados Unidos, França e Holanda. Diniz já recebeu vários prêmios e suas fotografias fazem parte de coleções de importantes instituições culturais.
Alice Lindorfer é uma fotógrafa de 22 anos que se formou em Fotografia em 2015 e tem seu próprio estúdio há um ano e meio. Ela sempre se identificou com a fotografia como forma de comunicação através de sentimentos e gosta de criar vínculos com os clientes fotografando as diferentes fases de suas vidas, incluindo newborns desde seu primeiro ensaio gratuito há três anos.
This document provides an introduction to using the Deducer graphical user interface (GUI) for R. It discusses loading and installing the Deducer and DeducerSurvival packages, exploring a sample dataset using the GUI's data viewer and generating frequency tables and graphs. Instructions are given on reading Excel data, converting variables to factors, and using the GUI to summarize categorical and continuous variables and add smoothing to graphs.
This document provides an overview and introduction to data mining using R and Rattle. It discusses data mining concepts and applications. It then introduces R as a programming language for statistical analysis and data mining. Rattle is presented as a graphical user interface tool built on R to make data mining more accessible. The document walks through installing and using Rattle to explore, visualize, model and evaluate data. It also discusses resources for learning more about R, Rattle and data mining.
This document provides instructions for installing R and R-Studio, two programs for performing advanced data analytics. It explains that R can be downloaded from its website for Linux, MacOS, and Windows, while R-Studio can also be downloaded from its website for those operating systems. The document then demonstrates how to use R-Studio, which displays the workspace, console, and ability to show graphics and other information across multiple tabs. It includes example R commands to help orient users and demonstrates assigning a value to a variable to show mastery of the basics.
Automating Machine Learning - Is it feasible?Manuel Martín
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
This document compares two integrated development environments (IDEs) for the R programming language: R-Studio and Rcmdr. R-Studio is a more powerful and flexible IDE that provides direct access to R code and facilitates interactions with R through its graphical interface. Rcmdr is simpler and more user-friendly, focusing on statistical analysis through buttons and menus. Both allow viewing data, but neither support data editing. The document provides guidelines for choosing between them and notes additional R IDEs under development.
MongoDB .local Chicago 2019: MongoDB – Powering the new age data demandsMongoDB
The document provides 5 client scenarios where MongoDB was leveraged to solve data and architecture challenges. Each scenario describes the client, problem to be solved, and how MongoDB was used. Key features highlighted across scenarios included MongoDB's schema-less design, high performance, data residency controls via sharding, flexible data models, and transaction support which enabled solutions for event streaming, machine learning, microservices architecture, and handling historical insurance data.
This document discusses key considerations for implementing a Laboratory Information Management System (LIMS). It begins by explaining how LIMS can help manage larger volumes of data and samples compared to tools like Excel. The document then outlines common stages of LIMS implementation from basic databases to more advanced custom systems. Key functionality that LIMS can provide is explored, such as sample tracking, integration with other systems, and security management. Important factors for a successful LIMS implementation are also summarized, such as defining business needs, customization costs, and user training.
AI as a Service, Build Shared AI Service Platforms Based on Deep Learning Tec...Databricks
I will share the vision and the production journey of how we build enterprise shared AI As A Service platforms with distributed deep learning technologies. Including those topics:
1) The vision of Enterprise Shared AI As A Service and typical AI services use cases at FinTech industry
2) The high level architecture design principles for AI As A Service
3) The technical evaluation journey to choose an enterprise deep learning framework with comparisons, such as why we choose Deep learning framework based on Spark ecosystem
4) Share some production AI use cases, such as how we implemented new Users-Items Propensity Models with deep learning algorithms with Spark,improve the quality , performance and accuracy of offer and campaigns design, targeting offer matching and linking etc.
5) Share some experiences and tips of using deep learning technologies on top of Spark , such as how we conduct Intel BigDL into a real production.
Building a Real-Time Security Application Using Log Data and Machine Learning...Sri Ambati
Building a Real-Time Security Application Using Log Data and Machine Learning- Karthik Aaravabhoomi
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses DevOps and MLOps practices for machine learning models. It outlines that while ML development shares some similarities with traditional software development, such as using version control and CI/CD pipelines, there are also key differences related to data, tools, and people. Specifically, ML requires additional focus on exploratory data analysis, feature engineering, and specialized infrastructure for training and deploying models. The document provides an overview of how one company structures their ML team and processes.
MongoDB World 2019: High Performance Auditing of Changes Based on MongoDB Cha...MongoDB
Take advantage of the elasticity of the cloud by creating resources that can heal themselves. Learn to create Compute Engine resources in GCP using Terraform that will install and configure a MongoDB replica set for you.
Comcast Labs Connect - PHLAI Conference Philadelphia 2018 Open Data Group
Open Data Group's Product Manager Rehgan Avon Speaks about Achieving AI and ML Operational Excellence at the Comcast Labs Connect - Artificial Intelligence And Machine Learning Conference in Philadelphia 2018.
Machine Learning - Eine Challenge für ArchitektenHarald Erb
Aufgrund vielfältiger potenzieller Geschäftschancen, die Machine Learning bietet, starten viele Unternehmen Initiativen für datengetriebene Innovationen. Dabei gründen sie Analytics-Teams, schreiben neue Stellen für Data Scientists aus, bauen intern Know-how auf und fordern von der IT-Organisation eine Infrastruktur für "heavy" Data Engineering & Processing samt Bereitstellung einer Analytics-Toolbox ein. Für IT-Architekten warten hier spannende Herausforderungen, u.a. bei der Zusammenarbeit mit interdisziplinären Teams, deren Mitglieder unterschiedlich ausgeprägte Kenntnisse im Bereich Machine Learning (ML) und Bedarfe bei der Tool-Unterstützung haben.
Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data analytics. Knowledge can take part in workflow design, constraint definition, parameter selection and configuration, human interactive and decision-making strategies. Here we present BIGOWL, an ontology to support knowledge management in Big Data analytics. BIGOWL is designed to cover a wide vocabulary of terms concerning Big Data analytics workflows, including their components and how they are connected, from data sources to the analytics visualization. It also takes into consideration aspects such as parameters, restrictions and formats. This ontology defines not only the taxonomic relationships between the different concepts, but also instances representing specific individuals to guide the users in the design of Big Data analytics workflows. For testing purposes, two case studies are developed, which consists in: first, real-world streaming processing with Spark of traffic Open Data, for route optimization in urban environment of New York city; and second, data mining classification of an academic dataset on local/cloud platforms. The analytics workflows resulting from the BIGOWL semantic model are validated and successfully evaluated.
Techniques for scaling application with security and visibility in cloudAkshay Mathur
Akshay Mathur gives a presentation on techniques for scaling applications with security and visibility in the cloud. He discusses 8 growth phases applications typically go through including load balancing, gaining insights, content optimization, offloading services, content switching, preventing bot traffic and DDoS attacks, continuous delivery, and the need for a unified cloud application front end solution to manage these phases. He introduces Appcito CAFE as a service that provides capabilities across availability, performance, security and DevOps to simplify application scaling in the cloud.
Story of Algolytic. Assisting companies with lots of data to get insights and predictions about customer behaviour, predicting risks based on statistical data analysis.
Story of how we can help, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
Story of our company - what we offer, how we changed from consulting company to product copmpany.
Overview of data analytics and predictive analytics solutions - such as Advanced Miner platform for complex data mining, Social Network analysis, Data quality tools etc.
Contact us for more info http://algolytics.com/blog/
check our news at the Blog: http://algolytics.com/blog/
An Introduction to Graph: Database, Analytics, and Cloud ServicesJean Ihm
Graph analysis employs powerful algorithms to explore and discover relationships in social network, IoT, big data, and complex transaction data. Learn how graph technologies are used in applications such as fraud detection for banking, customer 360, public safety, and manufacturing. This session will provide an overview and demos of graph technologies for Oracle Cloud Services, Oracle Database, NoSQL, Spark and Hadoop, including PGX analytics and PGQL property graph query language.
Presented at Analytics and Data Summit, March 20, 2018
This document provides an introduction to machine learning concepts and tools. It begins with an overview of what will be covered in the course, including machine learning types, algorithms, applications, and mathematics. It then discusses data science concepts like feature engineering and the typical steps in a machine learning project, including collecting and examining data, fitting models, evaluating performance, and deploying models. Finally, it reviews common machine learning tools and terminologies and where to find datasets.
Anuj Vaghani presented on his internship experience working with data analytics and machine learning teams. He discussed key concepts like data analytics, machine learning, and the methodology he used. Anuj completed two projects - one analyzing hotel booking data to understand cancellation factors, and another predicting bike demand using regression models. He found factors like booking lead time and deposit type influenced cancellations. For bike demand, random forest and gradient boosting models achieved high accuracy. Anuj concluded by discussing future areas like deep learning and new opportunities in the field.
Making advertising personal, 4th NL Recommenders MeetupOlivier Koch
Criteo is a performance advertising company that buys ad inventory and sells clicks at scale. They use real-time personalized product recommendations to select which ads to display to each user from billions of products. Their recommendation system retrieves candidate products for each user based on their browsing history and scores products from multiple data sources to select the top recommendations within 8 milliseconds to support their high traffic levels across many servers and data centers globally. They discuss challenges maintaining large user profiles, improving product data, and optimizing response time and independence of recommendations.
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Denodo
Watch full webinar here: https://bit.ly/35FUn32
Presented at CDAO New Zealand
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python, and Scala put advanced techniques at the fingertips of the data scientists.
However, most architecture laid out to enable data scientists miss two key challenges:
- Data scientists spend most of their time looking for the right data and massaging it into a usable format
- Results and algorithms created by data scientists often stay out of the reach of regular data analysts and business users
Watch this session on-demand to understand how data virtualization offers an alternative to address these issues and can accelerate data acquisition and massaging. And a customer story on the use of Machine Learning with data virtualization.
Watch here: https://bit.ly/3i2iJbu
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
Join us for an exciting session that will cover:
- The most interesting trends in data management.
- Our predictions on how those trends will change the data management world.
- How these trends are shaping the future of data virtualization and our own software.
SharePoint Site Redesign : Information Architecture and User-centered Design ...arsathe
The Rockwell Automation Technical Communications Team presented their SharePoint redesign project at the Cleveland User Experience Professionals Association (Cleveland-UXPA) meeting on September 26th, 2013.
Similar to H2O Machine Learning AutoML Roadmap 2016.10 (20)
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.