Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
The Machine Learning Workflow with AzureIvo Andreev
Machine learning is not black magic but a discipline that involves data analysis, data science and of course – hard work. From searching patterns in data, applying algorithms to converting to usable predictions, you would need background and appropriate tools. In this session, we will go through major approaches to prepare data, build and deploy ML models in Azure (ML Studio, DataScience VM, Jupyter Notebook). Most importantly – based on some examples from the real world, we will provide you with a workflow of best practices.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
The Machine Learning Workflow with AzureIvo Andreev
Machine learning is not black magic but a discipline that involves data analysis, data science and of course – hard work. From searching patterns in data, applying algorithms to converting to usable predictions, you would need background and appropriate tools. In this session, we will go through major approaches to prepare data, build and deploy ML models in Azure (ML Studio, DataScience VM, Jupyter Notebook). Most importantly – based on some examples from the real world, we will provide you with a workflow of best practices.
A presentation covers how data science is connected to build effective machine learning solutions. How to build end to end solutions in Azure ML. How to build, model, and evaluate algorithms in Azure ML.
Azure Machine Learning and ML on PremisesIvo Andreev
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis.Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API - now depricated and replaced by Google ML engine based on TensorFlow. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS in EE department from National Taiwan University, an MS-EE from the University of Michigan, and an MS in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, merit paper award in TAAI 2010, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup 2016 Champion. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He receives the Young Scholars' Creativity Award from Foundation for the Advancement of Outstanding Scholarship and Ta-You Wu Memorial Award.
Building a performing Machine Learning model from A to ZCharles Vestur
A 1-hour read to become highly knowledgeable about Machine learning and the machinery underneath, from scratch!
A presentation introducing to all fundamental concepts of Machine Learning step by step, following a classical approach to build a performing model. Simple examples and illustrations are used all along the presentation to make the concepts easier to grasp.
Automating your own Machine Learning Projects - Workshop: Working with the Masters.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Target leakage is one of the most difficult problems in developing real-world machine learning models. Leakage occurs when the training data gets contaminated with information that will not be known at prediction time. Additionally, there can be multiple sources of leakage, from data collection and feature engineering to partitioning and model validation. As a result, even experienced data scientists can inadvertently introduce leaks and become overly optimistic about the performance of the models they deploy. In this talk, we will look through real-life examples of data leakage at different stages of the data science project lifecycle, and discuss various countermeasures and best practices for model validation.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
From Labelling Open data images to building a private recommender systemPierre Gutierrez
Recommender systems are paramount for e-business companies. There is an increasing need to take into account all the user information to tailor the best product proposition. One of them is the content that the user actually sees: the visual of the product.
When it comes to hostels, some people can be more attracted by pictures of the room, the building or even the nearby beach.
In this talk, we will describe how we improved an e-business vacation retailer recommender system using the content of images. We’ll explain how to leverage open dataset and pre-trained deep learning models to derive user taste information. This transfer learning approach enables companies to use state of the art machine learning methods without having deep learning expertise.
Presented 2015-08-24 at SF Bay ACM, held at the eBay south campus in San Jose.
http://meetup.com/SF-Bay-ACM/events/221693508/
Project Jupiter https://jupyter.org/ evolved from IPython notebooks, and now supports a wide variety of programming language back-ends. Notebooks have proven to be effective tools used in Data Science, providing convenient packages for what Don Knuth coined as "literate programming" in the 1980s: code plus exposition in markdown. Results of running the code appear in-line as interactive graphics -- all packaged as collaborative, web-based documents. Some have said that the introduction of cloud-based notebooks is nearly as large of a fundamental change in software practice as the introduction of spreadsheets.
O'Reilly Media has been considering the question, "What comes after books and video?" Or, as one might imagine more pointedly, what comes after Kindle? To that point we have collaborated with Project Jupyter to integrate notebooks into our content management process, allowing authors to generate articles, tutorials, reports, and other media products as notebooks that also incorporate video segments. Code dependencies are containerized using Docker, and all of the content gets managed in Git repositories. We have added another layer, an open source project called Thebe that provides a kind of "media player" for embedding the containerized notebooks into web pages
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
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.
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Automating your own Machine Learning Projects - Workshop: Working with the Masters.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Target leakage is one of the most difficult problems in developing real-world machine learning models. Leakage occurs when the training data gets contaminated with information that will not be known at prediction time. Additionally, there can be multiple sources of leakage, from data collection and feature engineering to partitioning and model validation. As a result, even experienced data scientists can inadvertently introduce leaks and become overly optimistic about the performance of the models they deploy. In this talk, we will look through real-life examples of data leakage at different stages of the data science project lifecycle, and discuss various countermeasures and best practices for model validation.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Valencian Summer School 2015
Day 2
Lecture 11
The Future of Machine Learning
José David Martín-Guerrero (IDAL, UV)
https://bigml.com/events/valencian-summer-school-in-machine-learning-2015
From Labelling Open data images to building a private recommender systemPierre Gutierrez
Recommender systems are paramount for e-business companies. There is an increasing need to take into account all the user information to tailor the best product proposition. One of them is the content that the user actually sees: the visual of the product.
When it comes to hostels, some people can be more attracted by pictures of the room, the building or even the nearby beach.
In this talk, we will describe how we improved an e-business vacation retailer recommender system using the content of images. We’ll explain how to leverage open dataset and pre-trained deep learning models to derive user taste information. This transfer learning approach enables companies to use state of the art machine learning methods without having deep learning expertise.
Presented 2015-08-24 at SF Bay ACM, held at the eBay south campus in San Jose.
http://meetup.com/SF-Bay-ACM/events/221693508/
Project Jupiter https://jupyter.org/ evolved from IPython notebooks, and now supports a wide variety of programming language back-ends. Notebooks have proven to be effective tools used in Data Science, providing convenient packages for what Don Knuth coined as "literate programming" in the 1980s: code plus exposition in markdown. Results of running the code appear in-line as interactive graphics -- all packaged as collaborative, web-based documents. Some have said that the introduction of cloud-based notebooks is nearly as large of a fundamental change in software practice as the introduction of spreadsheets.
O'Reilly Media has been considering the question, "What comes after books and video?" Or, as one might imagine more pointedly, what comes after Kindle? To that point we have collaborated with Project Jupyter to integrate notebooks into our content management process, allowing authors to generate articles, tutorials, reports, and other media products as notebooks that also incorporate video segments. Code dependencies are containerized using Docker, and all of the content gets managed in Git repositories. We have added another layer, an open source project called Thebe that provides a kind of "media player" for embedding the containerized notebooks into web pages
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Usage of AI and machine learning models is likely to become more commonplace as larger swaths of the economy embrace automation and data-driven decision-making. While these predictive systems can be quite accurate, they have been treated as inscrutable black boxes in the past, that produce only numeric predictions with no accompanying explanations. Unfortunately, recent studies and recent events have drawn attention to mathematical and sociological flaws in prominent weak AI and ML systems, but practitioners usually don’t have the right tools to pry open machine learning black-boxes and debug them.
This presentation introduces several new approaches to that increase transparency, accountability, and trustworthiness in machine learning models. If you are a data scientist or analyst and you want to explain a machine learning model to your customers or managers (or if you have concerns about documentation, validation, or regulatory requirements), then this presentation is for you!
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.
HR Analytics: Using Machine Learning to Predict Employee Turnover - Matt Danc...Sri Ambati
Presented at #H2OWorld 2017 in Mountain View, CA.
Enjoy the video: https://youtu.be/-qfEOwm5Th4.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
- - -
In this talk, we discuss how we implemented H2O and LIME to predict and explain employee turnover on the IBM Watson HR Employee Attrition dataset. We use H2O’s new automated machine learning algorithm to improve on the accuracy of IBM Watson. We use LIME to produce feature importance and ultimately explain the black-box model produced by H2O.
Matt Dancho is the founder of Business Science (www.business-science.io), a consulting firm that assists organizations in applying data science to business applications. He is the creator of R packages tidyquant and timetk and has been working with data science for business and financial analysis since 2011. Matt holds master’s degrees in business and engineering, and has extensive experience in business intelligence, data mining, time series analysis, statistics and machine learning. Connect with Matt on twitter (https://twitter.com/mdancho84) and LinkedIn (https://www.linkedin.com/in/mattdancho/).
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
Slides originating from a talk I gave at ScalaMUC on 2013-12-17. The corresponding code can be found at https://github.com/afwlehmann/iteratee-tutorial .
Web pages can get very complex and slow. In this talk, I share how we solve some of these problems at LinkedIn by leveraging composition and streaming in the Play Framework. This was my keynote for Ping Conference 2014 ( http://www.ping-conf.com/ ): the video is on ustream ( http://www.ustream.tv/recorded/42801129 ) and the sample code is on github ( https://github.com/brikis98/ping-play ).
Creating an end-to-end Recommender System with Apache Spark and Elasticsearch...sparktc
At the sold-out Spark & Machine Learning Meetup in Brussels on October 27, 2016, Nick Pentreath of the Spark Technology Center teamed up with Jean-François Puget of IBM Analytics to deliver a talk called Creating an end-to-endRecommender System with Apache Spark and Elasticsearch.
Jean-François and Nick started with a look at the workflow for recommender systems and machine learning, then moved on to data modeling and using Spark ML for collaborative filtering. They closed with a discussion of deploying and scoring the recommender models, including a demo.
Detecting Hacks: Anomaly Detection on Networking DataJames Sirota
See https://medium.com/@jamessirota for a series of blog entries that goes with this deck...
Defense in Depth for Big Data
Network Anomaly Detection Overview
Volume Anomaly Detection
Feature Anomaly Detection
Model Architecture
Deployment on OpenSOC Platform
Questions
Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...MLconf
Why Machine Learning Algorithms Fall Short (And What You Can Do About It): Many think that machine learning is all about the algorithms. Want a self-learning system? Get your data, start coding or hire a PhD that will build you a model that will stand the test of time. Of course we know that this is not enough. Models degrade over time, algorithms that work great on yesterday’s data may not be the best option, new data sources and types are made available. In short, your self-learning system may not be learning anything at all. In this session, we will examine how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time. We will show how to apply mathematical optimization algorithms that often prove superior to local optimization methods favored by typical machine learning applications and discuss why these methods can crate better results. We will also examine the role of smart automation in the context of machine learning and how smart automation can create self-learning systems that are built to last.
Video: http://joyent.com/blog/linux-performance-analysis-and-tools-brendan-gregg-s-talk-at-scale-11x ; This talk for SCaLE11x covers system performance analysis methodologies and the Linux tools to support them, so that you can get the most out of your systems and solve performance issues quickly. This includes a wide variety of tools, including basics like top(1), advanced tools like perf, and new tools like the DTrace for Linux prototypes.
BigData: My Learnings from data analytics at Uber
Reference (highly recommended):
* Designing Data-Intensive Applications http://bit.ly/big_data_architecture
* Big Data and Machine Learning using Python tools http://bit.ly/big_data_machine_learning
* Uber Engineering Blog http://eng.uber.com
* Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale
http://bit.ly/hadoop_guide_bigdata
Brochure data science learning path board-infinity (1)NirupamNishant2
Board Infinity is a best digital marketing and data science institute in mumbai, which is a full-stack career platform for students and jobseekers enabled by personalised learning paths,career coaches and access to various job oppurtunities. We provide online and offline training in Data Science, Digital Marketing, Full stack Web Development,Product management< machine learning and Atrificial Intelligence,Online career counselling and other career solutions
IIPGH Webinar 1: Getting Started With Data Scienceds4good
In this webinar for ICT Professionals Ghana, we explore the concepts of data science and its motivations as a recent specialization. creating the background for how Artificial Intelligence relates to Machine Learning and to Deep Learning. We further discuss the data science technology stack and the opportunities that exist in the space.
Machine learning applications are typically stitched together from hopes and dreams, shell scripts, cron jobs, home-grown schedulers, snippets of configuration clipped from multiple blog posts, thousands of hard-coded business rules, a.k.a. "our SQL corpus," and a few lines of training and testing code. Organizing all the moving parts into something maintainable and supportive of ongoing development is a challenge most teams have on their TODO list, roadmap, or tech debt pile. Getting ahead of the day-to-day demands and settling into a sane architecture often seems like an unattainable goal. The past several years have seen an explosion of tool-building in the data engineering and analytics area, including in Apache projects spanning the areas of search and information retrieval, job orchestration, file and stream formats, and machine learning libraries. In this talk we will cover our product and development teams' choices of architecture and tools, from data ingestion and storage, through transformations and processing, to presentation of results and publishing to web services, reports, and applications.
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
You are experiencing the benefits of machine learning everyday through product recommendations on Amazon & Bol.com, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.
Has your app taken off? Are you thinking about scaling? MongoDB makes it easy to horizontally scale out with built-in automatic sharding, but did you know that sharding isn't the only way to achieve scale with MongoDB?
In this webinar, we'll review three different ways to achieve scale with MongoDB. We'll cover how you can optimize your application design and configure your storage to achieve scale, as well as the basics of horizontal scaling. You'll walk away with a thorough understanding of options to scale your MongoDB application.
Topics covered include:
- Scaling Vertically
- Hardware Considerations
- Index Optimization
- Schema Design
- Sharding
Want to know more about Common Data Model and Service? You need to understant what's the difference between CDS for Apps and Analytics? Feel free to use these slides and send me your feed backs.
Predictive analytics is touching more and more lives every day. Machine Learning lets you predict and change the future. Do you know that Microsoft products like Xbox and Bing integrate some machine learning capabilities in their workflows? Come to the session and take a look of the new cloud-based machine learning platform called AzureML from a BI architect perspective, without all the data scientist knowledge.
There are patterns for things such as domain-driven design, enterprise architectures, continuous delivery, microservices, and many others.
But where are the data science and data engineering patterns?
Sometimes, data engineering reminds me of cowboy coding - many workarounds, immature technologies and lack of market best practices.
State of Play. Data Science on Hadoop in 2015 by SEAN OWEN at Big Data Spain ...Big Data Spain
http://www.bigdataspain.org/2014/conference/state-of-play-data-science-on-hadoop-in-2015-keynote
Machine Learning is not new. Big Machine Learning is qualitatively different: More data beats algorithm improvement, scale trumps noise and sample size effects, can brute-force manual tasks.
Session presented at Big Data Spain 2014 Conference
18th Nov 2014
Kinépolis Madrid
http://www.bigdataspain.org
Event promoted by: http://www.paradigmatecnologico.com
Slides: https://speakerdeck.com/bigdataspain/state-of-play-data-science-on-hadoop-in-2015-by-sean-owen-at-big-data-spain-2014
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
NLP & Machine Learning - An Introductory Talk Vijay Ganti
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
Similar to Data Workflows for Machine Learning - SF Bay Area ML (20)
Human in the loop: a design pattern for managing teams working with MLPaco Nathan
Strata CA 2018-03-08
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/64223
Although it has long been used for has been used for use cases like simulation, training, and UX mockups, human-in-the-loop (HITL) has emerged as a key design pattern for managing teams where people and machines collaborate. One approach, active learning (a special case of semi-supervised learning), employs mostly automated processes based on machine learning models, but exceptions are referred to human experts, whose decisions help improve new iterations of the models.
Human-in-the-loop: a design pattern for managing teams that leverage MLPaco Nathan
Strata Singapore 2017 session talk 2017-12-06
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/65611
Human-in-the-loop is an approach which has been used for simulation, training, UX mockups, etc. A more recent design pattern is emerging for human-in-the-loop (HITL) as a way to manage teams working with machine learning (ML). A variant of semi-supervised learning called active learning allows for mostly automated processes based on ML, where exceptions get referred to human experts. Those human judgements in turn help improve new iterations of the ML models.
This talk reviews key case studies about active learning, plus other approaches for human-in-the-loop which are emerging among AI applications. We’ll consider some of the technical aspects — including available open source projects — as well as management perspectives for how to apply HITL:
* When is HITL indicated vs. when isn’t it applicable?
* How do HITL approaches compare/contrast with more “typical” use of Big Data?
* What’s the relationship between use of HITL and preparing an organization to leverage Deep Learning?
* Experiences training and managing a team which uses HITL at scale
* Caveats to know ahead of time:
* In what ways do the humans involved learn from the machines?
* In particular, we’ll examine use cases at O’Reilly Media where ML pipelines for categorizing content are trained by subject matter experts providing examples, based on HITL and leveraging open source [Project Jupyter](https://jupyter.org/ for implementation).
Human-in-a-loop: a design pattern for managing teams which leverage MLPaco Nathan
Human-in-a-loop: a design pattern for managing teams which leverage ML
Big Data Spain, 2017-11-16
https://www.bigdataspain.org/2017/talk/human-in-the-loop-a-design-pattern-for-managing-teams-which-leverage-ml
Human-in-the-loop is an approach which has been used for simulation, training, UX mockups, etc. A more recent design pattern is emerging for human-in-the-loop (HITL) as a way to manage teams working with machine learning (ML). A variant of semi-supervised learning called _active learning_ allows for mostly automated processes based on ML, where exceptions get referred to human experts. Those human judgements in turn help improve new iterations of the ML models.
This talk reviews key case studies about active learning, plus other approaches for human-in-the-loop which are emerging among AI applications. We'll consider some of the technical aspects -- including available open source projects -- as well as management perspectives for how to apply HITL:
* When is HITL indicated vs. when isn't it applicable?
* How do HITL approaches compare/contrast with more "typical" use of Big Data?
* What's the relationship between use of HITL and preparing an organization to leverage Deep Learning?
* Experiences training and managing a team which uses HITL at scale
* Caveats to know ahead of time
* In what ways do the humans involved learn from the machines?
In particular, we'll examine use cases at O'Reilly Media where ML pipelines for categorizing content are trained by subject matter experts providing examples, based on HITL and leveraging open source [Project Jupyter](https://jupyter.org/ for implementation).
Humans in a loop: Jupyter notebooks as a front-end for AIPaco Nathan
JupyterCon NY 2017-08-24
https://www.safaribooksonline.com/library/view/jupytercon-2017-/9781491985311/video313210.html
Paco Nathan reviews use cases where Jupyter provides a front-end to AI as the means for keeping "humans in the loop". This talk introduces *active learning* and the "human-in-the-loop" design pattern for managing how people and machines collaborate in AI workflows, including several case studies.
The talk also explores how O'Reilly Media leverages AI in Media, and in particular some of our use cases for active learning such as disambiguation in content discovery. We're using Jupyter as a way to manage active learning ML pipelines, where the machines generally run automated until they hit an edge case and refer the judgement back to human experts. In turn, the experts training the ML pipelines purely through examples, not feature engineering, model parameters, etc.
Jupyter notebooks serve as one part configuration file, one part data sample, one part structured log, one part data visualization tool. O'Reilly has released an open source project on GitHub called `nbtransom` which builds atop `nbformat` and `pandas` for our active learning use cases.
This work anticipates upcoming work on collaborative documents in JupyterLab, based on Google Drive. In other words, where the machines and people are collaborators on shared documents.
Humans in the loop: AI in open source and industryPaco Nathan
Nike Tech Talk, Portland, 2017-08-10
https://niketechtalks-aug2017.splashthat.com/
O'Reilly Media gets to see the forefront of trends in artificial intelligence: what the leading teams are working on, which use cases are getting the most traction, previews of advances before they get announced on stage. Through conferences, publishing, and training programs, we've been assembling resources for anyone who wants to learn. An excellent recent example: Generative Adversarial Networks for Beginners, by Jon Bruner.
This talk covers current trends in AI, industry use cases, and recent highlights from the AI Conf series presented by O'Reilly and Intel, plus related materials from Safari learning platform, Strata Data, Data Show, and the upcoming JupyterCon.
Along with reporting, we're leveraging AI in Media. This talk dives into O'Reilly uses of deep learning -- combined with ontology, graph algorithms, probabilistic data structures, and even some evolutionary software -- to help editors and customers alike accomplish more of what they need to do.
In particular, we'll show two open source projects in Python from O'Reilly's AI team:
• pytextrank built atop spaCy, NetworkX, datasketch, providing graph algorithms for advanced NLP and text analytics
• nbtransom leveraging Project Jupyter for a human-in-the-loop design pattern approach to AI work: people and machines collaborating on content annotation
Lessons learned from 3 (going on 4) generations of Jupyter use cases at O'Reilly Media. In particular, about "Oriole" tutorials which combine video with Jupyter notebooks, Docker containers, backed by services managed on a cluster by Marathon, Mesos, Redis, and Nginx.
https://conferences.oreilly.com/fluent/fl-ca/public/schedule/detail/62859
https://conferences.oreilly.com/velocity/vl-ca/public/schedule/detail/62858
Strata UK 2017. Computable content leverages Jupyter notebooks to make learning materials more powerful by integrating compute engines, data sources, etc. O’Reilly Media extended this approach to create the new Oriole Online Tutorial medium, publishing notebooks from authors along with video timelines. (A free public tutorial, Regex Golf, by Peter Norvig demonstrates what’s possible with this technology integration.) Each user session launches a Docker container on a Mesos cluster for fully personalized compute environments. The UX is entirely browser based.
See 2020 update: https://derwen.ai/s/h88s
SF Python Meetup, 2017-02-08
https://www.meetup.com/sfpython/events/237153246/
PyTextRank is a pure Python open source implementation of *TextRank*, based on the [Mihalcea 2004 paper](http://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf) -- a graph algorithm which produces ranked keyphrases from texts. Keyphrases generally more useful than simple keyword extraction. PyTextRank integrates use of `TextBlob` and `SpaCy` for NLP analysis of texts, including full parse, named entity extraction, etc. It also produces auto-summarization of texts, making use of an approximation algorithm, `MinHash`, for better performance at scale. Overall, the package is intended to complement machine learning approaches -- specifically deep learning used for custom search and recommendations -- by developing better feature vectors from raw texts. This package is in production use at O'Reilly Media for text analytics.
Use of standards and related issues in predictive analyticsPaco Nathan
My presentation at KDD 2016 in SF, in the "Special Session on Standards in Predictive Analytics In the Era of Big and Fast Data" morning track about PMML and PFA http://dmg.org/kdd2016.html
GalvanizeU Seattle: Eleven Almost-Truisms About DataPaco Nathan
http://www.meetup.com/Seattle-Data-Science/events/223445403/
Almost a dozen almost-truisms about Data that almost everyone should consider carefully as they embark on a journey into Data Science. There are a number of preconceptions about working with data at scale where the realities beg to differ. This talk estimates that number to be at least eleven, through probably much larger. At least that number has a great line from a movie. Let's consider some of the less-intuitive directions in which this field is heading, along with likely consequences and corollaries -- especially for those who are just now beginning to study about the technologies, the processes, and the people involved.
Microservices, containers, and machine learningPaco Nathan
http://www.oscon.com/open-source-2015/public/schedule/detail/41579
In this presentation, an open source developer community considers itself algorithmically. This shows how to surface data insights from the developer email forums for just about any Apache open source project. It leverages advanced techniques for natural language processing, machine learning, graph algorithms, time series analysis, etc. As an example, we use data from the Apache Spark email list archives to help understand its community better; however, the code can be applied to many other communities.
Exsto is an open source project that demonstrates Apache Spark workflow examples for SQL-based ETL (Spark SQL), machine learning (MLlib), and graph algorithms (GraphX). It surfaces insights about developer communities from their email forums. Natural language processing services in Python (based on NLTK, TextBlob, WordNet, etc.), gets containerized and used to crawl and parse email archives. These produce JSON data sets, then we run machine learning on a Spark cluster to find out insights such as:
* What are the trending topic summaries?
* Who are the leaders in the community for various topics?
* Who discusses most frequently with whom?
This talk shows how to use cloud-based notebooks for organizing and running the analytics and visualizations. It reviews the background for how and why the graph analytics and machine learning algorithms generalize patterns within the data — based on open source implementations for two advanced approaches, Word2Vec and TextRank The talk also illustrates best practices for leveraging functional programming for big data.
https://www.eventbrite.com/e/talk-by-paco-nathan-graph-analytics-in-spark-tickets-17173189472
Big Brains meetup hosted by BloomReach, 2015-06-04
Case study / demo of a large-scale graph analytics project, leveraging GraphX in Apache Spark to surface insights about open source developer communities — based on data mining of their email forums. The project works with any Apache email archive, applying NLP and machine learning techniques to analyze message threads, then constructs a large graph. Graph analytics, based on concise Scala coding examples in Spark, surface themes and interactions within the community. Results are used as feedback for respective developer communities, such as leaderboards, etc. As an example, we will examine analysis of the Spark developer community itself.
QCon São Paulo: Real-Time Analytics with Spark StreamingPaco Nathan
"Real-Time Analytics with Spark Streaming" presented at QCon São Paulo, 2015-03-26
http://qconsp.com/presentation/real-time-analytics-spark-streaming
This talk presents an overview of Spark and its history and applications, then focuses on the Spark Streaming component used for real-time analytics. We compare it with earlier frameworks such as MillWheel and Storm, and explore industry motivations for open-source micro-batch streaming at scale.
The talk will include demos for streaming apps that include machine-learning examples. We also consider public case studies of production deployments at scale.
We’ll review the use of open-source sketch algorithms and probabilistic data structures that get leveraged in streaming – for example, the trade-off of 4% error bounds on real-time metrics for two orders of magnitude reduction in required memory footprint of a Spark app.
Strata 2015 Data Preview: Spark, Data Visualization, YARN, and MorePaco Nathan
Spark and Databricks component of the O'Reilly Media webcast "2015 Data Preview: Spark, Data Visualization, YARN, and More", as a preview of the 2015 Strata + Hadoop World conference in San Jose http://www.oreilly.com/pub/e/3289
Microservices, Containers, and Machine LearningPaco Nathan
Session talk for Data Day Texas 2015, showing GraphX and SparkSQL for text analytics and graph analytics of an Apache developer email list -- including an implementation of TextRank in Spark.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Data Workflows for Machine Learning - SF Bay Area ML
1. Data Workflows for
Machine Learning:
SF Bay Area ML Meetup
2014-04-09
!
Paco Nathan
@pacoid
http://liber118.com/pxn/
meetup.com/SF-Bayarea-Machine-Learning/events/173759442/
2. BTW, since it’s April 9th…
Happy #IoTDay !!
http://iotday.org/
!
(any idea of what this means?)
3. Why is this talk here?
Machine Learning in production apps is less and less about
algorithms (even though that work is quite fun and vital)
!
Performing real work is more about:
! socializing a problem within an organization
! feature engineering (“Beyond Product Managers”)
! tournaments in CI/CD environments
! operationalizing high-ROI apps at scale
! etc.
!
So I’ll just crawl out on a limb and state that leveraging great
frameworks to build data workflows is more important than
chasing after diminishing returns on highly nuanced algorithms.
!
Because Interwebs!
4. Data Workflows for Machine Learning
Middleware has been evolving for Big Data, and there are some
great examples — we’ll review several. Process has been evolving
too, right along with the use cases.
!
Popular frameworks typically provide some Machine Learning
capabilities within their core components, or at least among their
major use cases.
!
Let’s consider features from Enterprise DataWorkflows as a basis
for what’s needed in Data Workflows for Machine Learning.
!
Their requirements for scale, robustness, cost trade-offs,
interdisciplinary teams, etc., serve as guides in general.
5. Caveat Auditor
I won’t claim to be expert with each of the frameworks and
environments described in this talk. Expert with a few of them
perhaps, but more to the point: embroiled in many use cases.
!
This talk attempts to define a “scorecard” for evaluating
important ML data workflow features:
what’s needed for use cases, compare and contrast of what’s
available, plus some indication of which frameworks are likely
to be best for a given scenario.
!
Seriously, this is a work in progress.
6. Outline
• Definition: Machine Learning
• Definition: Data Workflows
• A whole bunch o’ examples across several platforms
• Nine points to discuss, leading up to a scorecard
• A crazy little thing called PMML
• Questions, comments, flying tomatoes…
8. “Machine learning algorithms can figure out how to perform
important tasks by generalizing from examples.This is often
feasible and cost-effective where manual programming is not.
As more data becomes available, more ambitious problems
can be tackled. As a result, machine learning is widely used
in computer science and other fields.”
Pedro Domingos, U Washington
A Few Useful Things to Know about Machine Learning
Definition: Machine Learning
• overfitting (variance)
• underfitting (bias)
• “perfect classifier” (no free lunch)
[learning as generalization] =
[representation] + [evaluation] + [optimization]
9. Definition: Machine Learning … Conceptually
!
!
1. real-world data
2. graph theory for representation
3. convert to sparse matrix for production work
leveraging abstract algebra + func programming
4. cost-effective parallel processing for ML apps
at scale, live use cases
10. Definition: Machine Learning … Conceptually
!
!
1. real-world data
2. graph theory for representation
3. convert to sparse matrix for production work
leveraging abstract algebra + func programming
4. cost-effective parallel processing for ML apps
at scale, live use cases
[representation]
[evaluation]
[optimization]
[generalization]
f(x): loss function
g(z): regularization term
12. Definition: Machine Learning … Process, Feature Engineering
evaluationrepresentation optimization use cases
feature engineering
data
sources
data
sources
data prep
pipeline
train
test
hold-outs
learners
classifiers
classifiers
classifiers
data
sources
scoring
13. Definition: Machine Learning … Process, Tournaments
evaluationrepresentation optimization use cases
feature engineering tournaments
data
sources
data
sources
data prep
pipeline
train
test
hold-outs
learners
classifiers
classifiers
classifiers
data
sources
scoring
quantify and measure:
• benefit?
• risk?
• operational costs?
• obtain other data?
• improve metadata?
• refine representation?
• improve optimization?
• iterate with stakeholders?
• can models (inference) inform
first principles approaches?
14. monoid (an alien life form, courtesy of abstract algebra):
! binary associative operator
! closure within a set of objects
! unique identity element
what are those good for?
! composable functions in workflows
! compiler “hints” on steroids, to parallelize
! reassemble results minimizing bottlenecks at scale
! reusable components, like Lego blocks
! think: Docker for business logic
Monoidify! Monoids as a Design Principle for Efficient
MapReduce Algorithms
Jimmy Lin, U Maryland/Twitter
kudos to Oscar Boykin, Sam Ritchie, et al.
Definition: Machine Learning … Invasion of the Monoids
15. Definition: Machine Learning
evaluationrepresentation optimization use cases
feature engineering tournaments
data
sources
data
sources
data prep
pipeline
train
test
hold-outs
learners
classifiers
classifiers
classifiers
data
sources
scoring
quantify and measure:
• benefit?
• risk?
• operational costs?
• obtain other data?
• improve metadata?
• refine representation?
• improve optimization?
• iterate with stakeholders?
• can models (inference) inform
first principles approaches?
discovery
discovery
modeling
modeling
integration
integration
appsapps
systems
systems
data
science
Data
Scientist
App Dev
Ops
Domain
Expert
introduced
capability
16. Definition
evaluationrepresentation optimization use cases
feature engineering tournaments
data
sources
data
sources
data prep
pipeline
train
test
hold-outs
learners
classifiers
classifiers
classifiers
data
sources
scoring
quantify and measure:
• benefit?
• risk?
• operational costs?
• obtain other data?
• improve metadata?
• refine representation?
• improve optimization?
• iterate with stakeholders?
• can models (inference) inform
first principles approaches?
discovery
discovery
modeling
modeling
integration
integration
appsapps
systems
systems
data
science
Data
Scientist
App Dev
Ops
Domain
Expert
introduced
capability
Can we fit the required
process into generalized
workflow definitions?
17. Definition: Data Workflows
Middleware, effectively, is evolving for Big Data and Machine Learning…
The following design pattern — a DAG — shows up in many places,
via many frameworks:
ETL
data
prep
predictive
model
data
sources
end
uses
18. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
uses
ANSI SQL for ETL
19. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
usesJava, Pig, etc., for business logic
20. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
uses
SAS for predictive models
21. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
uses
SAS for predictive models
ANSI SQL for ETL most of the licensing costs…
22. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
usesJava, Pig, etc.,
most of the project costs…
23. Definition: Data Workflows
definition of a typical Enterprise workflow which crosses through
multiple departments, languages, and technologies…
ETL
data
prep
predictive
model
data
sources
end
usesJava, Pig, etc.,
most of the project costs…
Something emerges
to fill these needs,
for instance…
24. ETL
data
prep
predictive
model
data
sources
end
uses
Lingual:
DW → ANSI SQL
Pattern:
SAS, R, etc. → PMML
business logic in Java,
Clojure, Scala, etc.
sink taps for
Memcached, HBase,
MongoDB, etc.
source taps for
Cassandra, JDBC,
Splunk, etc.
Definition: Data Workflows
For example, Cascading and related projects implement the following
components, based on 100% open source: cascading.org
a compiler sees it all…
one connected DAG:
• troubleshooting
• exception handling
• notifications
• some optimizations
25. ETL
data
prep
predictive
model
data
sources
end
uses
Lingual:
DW → ANSI SQL
Pattern:
SAS, R, etc. → PMML
business logic in Java,
Clojure, Scala, etc.
sink taps for
Memcached, HBase,
MongoDB, etc.
source taps for
Cassandra, JDBC,
Splunk, etc.
Definition: Data Workflows
Cascading allows multiple departments to combine their workflow components
into an integrated app – one among many, typically – based on 100% open source
!
!
FlowDef flowDef = FlowDef.flowDef()!
.setName( "etl" )!
.addSource( "example.employee", emplTap )!
.addSource( "example.sales", salesTap )!
.addSink( "results", resultsTap );!
!
SQLPlanner sqlPlanner = new SQLPlanner()!
.setSql( sqlStatement );!
!
flowDef.addAssemblyPlanner( sqlPlanner );!
!
!
cascading.org
26. ETL
data
prep
predictive
model
data
sources
end
uses
Lingual:
DW → ANSI SQL
Pattern:
SAS, R, etc. → PMML
business logic in Java,
Clojure, Scala, etc.
sink taps for
Memcached, HBase,
MongoDB, etc.
source taps for
Cassandra, JDBC,
Splunk, etc.
Definition: Data Workflows
Cascading allows multiple departments to combine their workflow components
into an integrated app – one among many, typically – based on 100% open source
!
!
FlowDef flowDef = FlowDef.flowDef()!
.setName( "classifier" )!
.addSource( "input", inputTap )!
.addSink( "classify", classifyTap );!
!
PMMLPlanner pmmlPlanner = new PMMLPlanner()!
.setPMMLInput( new File( pmmlModel ) )!
.retainOnlyActiveIncomingFields();!
!
flowDef.addAssemblyPlanner( pmmlPlanner );!
!
!
27. Enterprise DataWorkflows with Cascading
O’Reilly, 2013
shop.oreilly.com/product/
0636920028536.do
ETL
data
prep
predictive
model
data
sources
end
uses
28. Enterprise DataWorkflows with Cascading
O’Reilly, 2013
shop.oreilly.com/product/
0636920028536.do
ETL
data
prep
predictive
model
data
sources
end
uses
This begs an update…
29. Enterprise DataWorkflows with Cascading
O’Reilly, 2013
shop.oreilly.com/product/
0636920028536.do
ETL
data
prep
predictive
model
data
sources
end
uses
Because…
32. Example: KNIME
“a user-friendly graphical workbench for the entire analysis
process: data access, data transformation, initial investigation,
powerful predictive analytics, visualisation and reporting.”
• large number of integrations (over 1000 modules)
• ranked #1 in customer satisfaction among
open source analytics frameworks
• visual editing of reusable modules
• leverage prior work in R, Perl, etc.
• Eclipse integration
• easily extended for new integrations
knime.org
33. Example: Python stack
Python has much to offer – ranging across an
organization, no just for the analytics staff
• ipython.org
• pandas.pydata.org
• scikit-learn.org
• numpy.org
• scipy.org
• code.google.com/p/augustus
• continuum.io
• nltk.org
• matplotlib.org
34. Example: Julia
“a high-level, high-performance dynamic programming language
for technical computing, with syntax that is familiar to users of
other technical computing environments”
• significantly faster than most alternatives
• built to leverage parallelism, cloud computing
• still relatively new — one to watch!
importall Base!
!
type BubbleSort <: Sort.Algorithm end!
!
function sort!(v::AbstractVector, lo::Int, hi::Int, ::BubbleSort, o::Sort.Ordering)!
while true!
clean = true!
for i = lo:hi-1!
if Sort.lt(o, v[i+1], v[i])!
v[i+1], v[i] = v[i], v[i+1]!
clean = false!
end!
end!
clean && break!
end!
return v!
end!
julialang.org
35. Example: Summingbird
“a library that lets you write streaming MapReduce programs
that look like native Scala or Java collection transformations
and execute them on a number of well-known distributed
MapReduce platforms like Storm and Scalding.”
• switch between Storm, Scalding (Hadoop)
• Spark support is in progress
• leverage Algebird, Storehaus, Matrix API, etc.
github.com/twitter/summingbird
def wordCount[P <: Platform[P]]!
(source: Producer[P, String], store: P#Store[String, Long]) =!
source.flatMap { sentence => !
toWords(sentence).map(_ -> 1L)!
}.sumByKey(store)
37. Example: Scalding
• extends the Scala collections API so that distributed lists
become “pipes” backed by Cascading
• code is compact, easy to understand
• nearly 1:1 between elements of conceptual flow diagram
and function calls
• extensive libraries are available for linear algebra, abstract
algebra, machine learning – e.g., Matrix API, Algebird, etc.
• significant investments by Twitter, Etsy, eBay, etc.
• less learning curve than Cascalog
• build scripts… those take a while to run :
github.com/twitter/scalding
38. Example: Cascalog
(ns impatient.core!
(:use [cascalog.api]!
[cascalog.more-taps :only (hfs-delimited)])!
(:require [clojure.string :as s]!
[cascalog.ops :as c])!
(:gen-class))!
!
(defmapcatop split [line]!
"reads in a line of string and splits it by regex"!
(s/split line #"[[](),.)s]+"))!
!
(defn -main [in out & args]!
(?<- (hfs-delimited out)!
[?word ?count]!
((hfs-delimited in :skip-header? true) _ ?line)!
(split ?line :> ?word)!
(c/count ?count)))!
!
; Paul Lam!
; github.com/Quantisan/Impatient
cascalog.org
39. Example: Cascalog
• implements Datalog in Clojure, with predicates backed
by Cascading – for a highly declarative language
• run ad-hoc queries from the Clojure REPL –
approx. 10:1 code reduction compared with SQL
• composable subqueries, used for test-driven
development (TDD) practices at scale
• Leiningen build: simple, no surprises, in Clojure itself
• more new deployments than other Cascading DSLs –
Climate Corp is largest use case: 90% Clojure/Cascalog
• has a learning curve, limited number of Clojure
developers
• aggregators are the magic, and those take effort to learn
cascalog.org
40. Example: Apache Spark
in-memory cluster computing,
by Matei Zaharia, RamVenkataraman, et al.
• intended to make data analytics fast to write and to run
• load data into memory and query it repeatedly, much
more quickly than with Hadoop
• APIs in Scala, Java and Python, shells in Scala and Python
• Shark (Hive on Spark), Spark Streaming (like Storm), etc.
• integrations with MLbase, Summingbird (in progress)
// word count!
val file = spark.textFile(“hdfs://…”)!
val counts = file.flatMap(line => line.split(” “))!
.map(word => (word, 1))!
.reduceByKey(_ + _)!
counts.saveAsTextFile(“hdfs://…”)
spark-project.org
41. Example: MLbase
“distributed machine learning made easy”
• sponsored by UC Berkeley EECS AMP Lab
• MLlib – common algorithms, low-level, written atop Spark
• MLI – feature extraction, algorithm dev atop MLlib
• ML Optimizer – automates model selection,
compiler/optimizer
• see article:
http://strata.oreilly.com/2013/02/mlbase-scalable-
machine-learning-made-accessible.html
!
mlbase.org
data = load("hdfs://path/to/als_clinical")!
// the features are stored in columns 2-10!
X = data[, 2 to 10]!
y = data[, 1]!
model = do_classify(y, X)
42. Example: Spark SQL
“blurs the lines between RDDs and relational tables”
intermix SQL commands to querying external data,
along with complex analytics, in a single app:
• allows SQL extensions based on MLlib
• Shark is being migrated to Spark SQL
Spark SQL: Manipulating Structured Data Using Spark
Michael Armbrust, Reynold Xin (2014-03-24)
databricks.com/blog/2014/03/26/Spark-
SQL-manipulating-structured-data-using-
Spark.html
people.apache.org/~pwendell/catalyst-
docs/sql-programming-guide.html
!
// data can be extracted from existing sources
// e.g., Apache Hive
val trainingDataTable = sql("""
SELECT e.action
u.age,
u.latitude,
u.logitude
FROM Users u
JOIN Events e
ON u.userId = e.userId""")
!
// since `sql` returns an RDD, the results above
// can be used in MLlib
val trainingData = trainingDataTable.map { row =>
val features = Array[Double](row(1), row(2), row(3))
LabeledPoint(row(0), features)
}
!
val model =
new LogisticRegressionWithSGD().run(trainingData)
spark-project.org
43. Example: Titan
distributed graph database,
by Matthias Broecheler, Marko Rodriguez, et al.
• scalable graph database optimized for storing and
querying graphs
• supports hundreds of billions of vertices and edges
• transactional database that can support thousands of
concurrent users executing complex graph traversals
• supports search through Lucene, ElasticSearch
• can be backed by HBase, Cassandra, BerkeleyDB
• TinkerPop native graph stack with Gremlin, etc.
// who is hercules' paternal grandfather?!
g.V('name','hercules').out('father').out('father').name
thinkaurelius.github.io/titan
44. Example: MBrace
“a .NET based software stack that enables easy large-scale
distributed computation and big data analysis.”
• declarative and concise distributed algorithms in
F# asynchronous workflows
• scalable for apps in private datacenters or public
clouds, e.g.,Windows Azure or Amazon EC2
• tooling for interactive, REPL-style deployment,
monitoring, management, debugging inVisual Studio
• leverages monads (similar to Summingbird)
• MapReduce as a library, but many patterns beyond
m-brace.net
let rec mapReduce (map: 'T -> Cloud<'R>)!
(reduce: 'R -> 'R -> Cloud<'R>)!
(identity: 'R)!
(input: 'T list) =!
cloud {!
match input with!
| [] -> return identity!
| [value] -> return! map value!
| _ ->!
let left, right = List.split input!
let! r1, r2 =!
(mapReduce map reduce identity left)!
<||>!
(mapReduce map reduce identity right)!
return! reduce r1 r2!
}
46. Data Workflows
Formally speaking, workflows include people (cross-dept)
and define oversight for exceptional data
…otherwise, data flow or pipeline would be more apt
…examples include Cascading traps, with exceptional tuples
routed to a review organization, e.g., Customer Support
includes people, defines
oversight for exceptional data
Hadoop
Cluster
source
tap
source
tap sink
tap
trap
tap
customer
profile DBsCustomer
Prefs
logs
logs
Logs
Data
Workflow
Cache
Customers
Support
Web
App
Reporting
Analytics
Cubes
sink
tap
Modeling PMML
1
47. Data Workflows
Footnote –
see also an excellent article related to this point
and the next:
!
Therbligs for data science:
A nuts and bolts framework for accelerating data work
Abe Gong @Jawbone
blog.abegong.com/2014/03/therbligs-for-data-science.html
strataconf.com/strata2014/public/schedule/detail/32291
brighttalk.com/webcast/9059/105119
!
!
!
includes people, defines
oversight for exceptional data
1+
48. Data Workflows
Workflows impose a separation of concerns, allowing
for multiple abstraction layers in the tech stack
…specify what is required, not how to accomplish it
…articulating the business logic
… Cascading leverages pattern language
…related notions from Knuth, literate programming
…not unlike BPM/BPEL for Big Data
…examples: IPython, R Markdown, etc.
separation of concerns, allows
for literate programming
2
49. Data Workflows
Footnote –
while discussing separation of concerns and a general
design pattern, let’s consider data workflows in a
broader context of probabilistic programming, since
business data is heterogeneous and structured…
the data for every domain is heterogeneous.
Paraphrasing Ginsberg:
I’ve seen the best minds of my generation destroyed by
madness, dragging themselves through quagmires of large
LCD screens filled with Intellij debuggers and database
command lines, yearning to fit real-world data into their
preferred “deterministic” tools…
separation of concerns, allows
for literate programming
2+
Probabilistic Programming:
Why,What, How,When
Beau Cronin
Strata SC (2014)
speakerdeck.com/beaucronin/
probabilistic-programming-
strata-santa-clara-2014
Why Probabilistic Programming
Matters
Rob Zinkov
Convex Optimized (2012-06-27)
zinkov.com/posts/
2012-06-27-why-prob-
programming-matters/
50. Data Workflows
Multiple abstraction layers in the tech stack are
needed, but still emerging
…feedback loops based on machine data
…optimizers feed on abstraction
…metadata accounting:
• track data lineage
• propagate schema
• model / feature usage
• ensemble performance
…app history accounting:
• util stats, mixing workloads
• heavy-hitters, bottlenecks
• throughput, latency
multiple abstraction layers
for metadata, feedback,
and optimization
Cluster
Scheduler
Planners/
Optimizers
Clusters
DSLs
Machine
Data
• app history
• util stats
• bottlenecksMixed
Topologies
Business
Process
Reusable
Components
Portable
Models
• data lineage
• schema propagation
• feature selection
• tournaments
3
51. Data Workflows
Multiple
needed, but still emerging
…feedback loops based on
…optimizers feed on abstraction
…metadata accounting:
• track data lineage
• propagate
• model / feature usage
• ensemble performance
…app history accounting:
• util stats, mixing workloads
• heavy-hitters, bottlenecks
• throughput, latency
multiple abstraction layers
for metadata, feedback,
and optimization
Cluster
Scheduler
Planners/
Optimizers
Clusters
DSLs
Machine
Data
• app history
• util stats
• bottlenecksMixed
Topologies
Business
Process
Reusable
Components
Portable
Models
• data lineage
• schema propagation
• feature selection
• tournaments
3
Um, will compilers
ever look like this??
52. Data Workflows
Workflows must provide for test, which is no simple
matter
…testing is required on three abstraction layers:
• model portability/evaluation, ensembles, tournaments
• TDD in reusable components and business process
• continuous integration / continuous deployment
…examples: Cascalog for TDD, PMML for model eval
…still so much more to improve
…keep in mind that workflows involve people, too
testing: model evaluation,
TDD, app deployment
4
Cluster
Scheduler
Planners/
Optimizers
Clusters
DSLs
Machine
Data
• app history
• util stats
• bottlenecksMixed
Topologies
Business
Process
Reusable
Components
Portable
Models
• data lineage
• schema propagation
• feature selection
• tournaments
53. Data Workflows
Workflows enable system integration
…future-proof integrations and scale-out
…build components and apps, not jobs and command lines
…allow compiler optimizations across the DAG,
i.e., cross-dept contributions
…minimize operationalization costs:
• troubleshooting, debugging at scale
• exception handling
• notifications, instrumentation
…examples: KNIME, Cascading, etc.
!
future-proof system
integration, scale-out, ops
Hadoop
Cluster
source
tap
source
tap sink
tap
trap
tap
customer
profile DBsCustomer
Prefs
logs
logs
Logs
Data
Workflow
Cache
Customers
Support
Web
App
Reporting
Analytics
Cubes
sink
tap
Modeling PMML
5
54. Data Workflows
Visualizing workflows, what a great idea.
…the practical basis for:
• collaboration
• rapid prototyping
• component reuse
…examples: KNIME wins best in category
…Cascading generates flow diagrams,
which are a nice start
visualizing allows people
to collaborate through code
6
55. Data Workflows
Abstract algebra, containerizing workflow metadata
…monoids, semigroups, etc., allow for reusable components
with well-defined properties for running in parallel at scale
…let business process be agnostic about underlying topologies,
with analogy to Linux containers (Docker, etc.)
…compose functions, take advantage of sparsity (monoids)
…because “data is fully functional” – FP for s/w eng benefits
…aggregators are almost always “magic”, now we can solve
for common use cases
…read Monoidify! Monoids as a Design Principle for Efficient
MapReduce Algorithms by Jimmy Lin
…Cascading introduced some notions of this circa 2007,
but didn’t make it explicit
…examples: Algebird in Summingbird, Simmer, Spark, etc.
abstract algebra and
functional programming
containerize business process
7
56. Data Workflows
Footnote –
see also two excellent talks related to this point:
!
Algebra for Analytics
speakerdeck.com/johnynek/algebra-for-analytics
Oscar Boykin, Strata SC (2014)
Add ALL theThings:
Abstract Algebra Meets Analytics
infoq.com/presentations/abstract-algebra-analytics
Avi Bryant, Strange Loop (2013)
abstract algebra and
functional programming
containerize business process
7+
57. Data Workflows
Workflow needs vary in time, and need to blend time
…something something batch, something something low-latency
…scheduling batch isn’t hard; scheduling low-latency is
computationally brutal (see the Omega paper)
…because people like saying “Lambda Architecture”,
it gives them goosebumps, or something
…because real-time means so many different things
…because batch windows are so 1964
…examples: Summingbird, Oryx
blend results from different
time scales: batch plus low-
latency
8
Big Data
Nathan Marz, James Warren
manning.com/marz
58. Data Workflows
Workflows may define contexts in which model selection
possibly becomes a compiler problem
…for example, see Boyd, Parikh, et al.
…ultimately optimizing for loss function + regularization term
…perhaps not ready for prime-time immediately
…examples: MLbase
optimize learners in context,
to make model selection
potentially a compiler problem
9
f(x): loss function
g(z): regularization term
59. Data Workflows
For one of the best papers about what workflows really
truly require, see Out of the Tar Pit by Moseley and Marks
bonus
60. Nine Points for Data Workflows — a wish list
1. includes people, defines oversight for exceptional data
2. separation of concerns, allows for literate programming
3. multiple abstraction layers for metadata, feedback, and
optimization
4. testing: model evaluation, TDD, app deployment
5. future-proof system integration, scale-out, ops
6. visualizing workflows allows people to collaborate
through code
7. abstract algebra and functional programming
containerize business process
8. blend results from different time scales:
batch plus low-latency
9. optimize learners in context, to make model
selection potentially a compiler problem
Cluster
Scheduler
Planners/
Optimizers
Clusters
DSLs
Machine
Data
• app history
• util stats
• bottlenecksMixed
Topologies
Business
Process
Reusable
Components
Portable
Models
• data lineage
• schema propagation
• feature selection
• tournaments
61.
62. Nine Points for Data Workflows — a scorecard
Spark,
MLbase
Oryx Summing!
bird
Cascalog Cascading
!
KNIME Py Data R
Markdown
MBrace
includes people,
exceptional data
separation of
concerns
multiple
abstraction layers
testing in depth
future-proof
system
integration
visualize to collab
can haz monoids
blends batch +
“real-time”
optimize learners
in context
can haz PMML ✔ ✔ ✔ ✔ ✔
64. • established XML standard for predictive model markup
• organized by Data Mining Group (DMG), since 1997
http://dmg.org/
• members: IBM, SAS, Visa, FICO, Equifax, NASA,
Microstrategy, Microsoft, etc.
• PMML concepts for metadata, ensembles, etc., translate
directly into workflow abstractions, e.g., Cascading
!
“PMML is the leading standard for statistical and data mining models and
supported by over 20 vendors and organizations.With PMML, it is easy
to develop a model on one system using one application and deploy the
model on another system using another application.”
PMML – an industry standard
wikipedia.org/wiki/Predictive_Model_Markup_Language
66. • Association Rules: AssociationModel element
• Cluster Models: ClusteringModel element
• Decision Trees: TreeModel element
• Naïve Bayes Classifiers: NaiveBayesModel element
• Neural Networks: NeuralNetwork element
• Regression: RegressionModel and GeneralRegressionModel elements
• Rulesets: RuleSetModel element
• Sequences: SequenceModel element
• SupportVector Machines: SupportVectorMachineModel element
• Text Models: TextModel element
• Time Series: TimeSeriesModel element
PMML – model coverage
ibm.com/developerworks/industry/library/ind-PMML2/
67. !
PMML in Action
Alex Guazzelli,Wen-Ching Lin,Tridivesh Jena
amazon.com/dp/1470003244
!
See also excellent resources at:
zementis.com/pmml.htm
PMML – further study
69. Pattern – model scoring
• originally intended as a general purpose, tuple-oriented
model scoring engine for JVM-based frameworks
• library plugs into Cascading (Hadoop), and ostensibly
Cascalog, Scalding, Storm, Spark, Summingbird, etc.
• dev forum:
https://groups.google.com/forum/#!forum/pattern-user
• original GitHub repo:
https://github.com/ceteri/pattern/
• somehow, the other fork became deeply intertwined
with Cascading dependencies…
• work in progress to integrate with other frameworks,
add models, and also train models at scale using Spark
70. ## train a RandomForest model!
!
f <- as.formula("as.factor(label) ~ .")!
fit <- randomForest(f, data_train, ntree=50)!
!
## test the model on the holdout test set!
!
print(fit$importance)!
print(fit)!
!
predicted <- predict(fit, data)!
data$predicted <- predicted!
confuse <- table(pred = predicted, true = data[,1])!
print(confuse)!
!
## export predicted labels to TSV!
!
write.table(data, file=paste(dat_folder, "sample.tsv", sep="/"),
quote=FALSE, sep="t", row.names=FALSE)!
!
## export RF model to PMML!
!
saveXML(pmml(fit), file=paste(dat_folder, "sample.rf.xml", sep="/"))!
Pattern – create a model in R
74. ## run an RF classifier at scale!
!
hadoop jar build/libs/pattern.jar !
data/sample.tsv out/classify out/trap !
--pmml data/sample.rf.xml!
!
!
Pattern – score a model, using pre-defined Cascading app
77. Nine Points for Data Workflows — a scorecard
Spark,
MLbase
Oryx Summing!
bird
Cascalog Cascading
!
KNIME Py Data R
Markdown
MBrace
includes people,
exceptional data
separation of
concerns
multiple
abstraction layers
testing in depth
future-proof
system
integration
visualize to collab
can haz monoids
blends batch +
“real-time”
optimize learners
in context
can haz PMML ✔ ✔ ✔ ✔ ✔
78. PMML – what’s needed?
in the language standard:
! data preparation
! data sources/sinks as parameters
! track data lineage
! more monoid, less XML
! handle data exceptions => TDD
! updates/active learning?
! tournaments?
!
in the workflow frameworks:
! include feature engineering w/ model?
! support evaluation better
! build ensembles
evaluationrepresentation optimization use cases
feature engineering tournaments
data
sources
data
sources
data prep
pipeline
train
test
hold-outs
learners
classifiers
classifiers
classifiers
data
sources
scoring
quantify and measure:
• benefit?
• risk?
• operational costs?
• obtain other data?
• improve metadata?
• refine representation?
• improve optimization?
• iterate with stakeholders?
• can models (inference) inform
first principles approaches?
79. Thank you, and…
stay tuned for
Just Enough Math
(Summer 2014 w/ Allen Day)
!
monthly newsletter for updates, events,
conference summaries, etc.:
liber118.com/pxn/