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Automatic Visualization with Driverless AI

We wish to read datasets (text, logs, relational tables, hierarchies, streams, images, ...) and display interesting aspects of their content. The design to do this rests on the grammar of graphics, scagnostics, and a modeler based on the logic of statistical analysis. We distinguish an automatic visualization system (AVS) from an automated visualization system. The former automatically makes decisions about what is to be visualized. The latter is a programming system for automating the production of charts, graphs and visualizations. An AVS is designed to provide a first glance at data before modeling and analysis are done. AVS is designed to protect researchers from ignoring missing data, outliers, miscodes and other anomalies that can violate statistical assumptions or otherwise jeopardize the validity of models. This webinar will cover the theory and operation of the AutoViz implementation of AVS inside Driverless AI.

Leland's Bio:
Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.
Recorded May 14 2018 57 mins
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Presented by
Leland Wilkinson
Presentation preview: Automatic Visualization with Driverless AI

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  • Scalable Machine Learning on KubeFlow with H2O.ai Aug 21 2018 6:00 pm UTC 75 mins
    Nick Png
    Nick will give an overview of H2O, the leading open source machine learning platform for the enterprise, which integrates seamlessly with R and Python environments, as well as, Driverless AI, an enterprise grade automated machine learning solution. H2O and Driverless AI provide best of breed scalable open source machine learning and automatic machine learning respectively.


    In this session, we will cover running H2O-3 and Driverless AI on GKE with Kubeflow. Additionally, we will be discussing burst to cloud and how to we can leverage Google Cloud Platform where local infrastructure is just not enough.
  • Peek Under the Hood of H2O Driverless AI with Auto Doc Recorded: Aug 15 2018 60 mins
    Megan Kurka, Vinod Iyengar
    H2O Driverless AI is H2O.ai's flagship platform for automatic machine learning. It fully automates the data science workflow including some of the most challenging tasks in applied data science such as feature engineering, model tuning, model optimization, and model deployment. Driverless AI turns Kaggle Grandmaster recipes into a full functioning platform that delivers "an expert data scientist in a box" from training to deployment.

    Driverless AI with Auto Doc is the next logical step of the data science workflow by taking the final step of automatically documenting and explaining the processes used by the platform. Auto Doc frees up the user from the time consuming task of documenting and summarizing their workflow while building machine learning models. The resulting documentation provides users with insight into machine learning workflow created by Driverless AI including details about the data used, the validation schema selected, model and feature tuning, and the final model created. With this capability in Driverless AI, users can focus on model insights and results.
  • Productionizing H2O Models with Apache Spark Recorded: Aug 2 2018 46 mins
    Jakub Hava
    Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library.

    Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs – Model Object Optimized – a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. In this talk we will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs.

    We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, we will show how to utilize pre-trained model MOJOs with Spark pipelines.
  • Time-Series in Driverless AI Recorded: Jul 25 2018 45 mins
    Marios Michailidis
    Driverless AI is H2O.ai's latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.

    Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.
  • Machine Learning in Reproductive Science: Human Embryo Selection and Beyond Recorded: Jul 16 2018 42 mins
    Oleksii Barash
    Description:

    In this talk, Oleksii Barash PhD, IVF Laboratory Research Director at the Reproductive Science Center of the San Francisco Bay Area, will discuss his team’s approach to applying machine learning for decision making during infertility treatment. Oleksii will also give a quick overview of how he uses Driverless AI to build models for predicting IVF outcomes and select the best embryo for embryo transfer.


    Speaker's Bio:

    Oleksii believes that evidence-based clinical decisions will greatly improve the efficiency and safety of the medicine. He received his Master degree in Clinical Embryology from University of Leeds (UK) and PhD in Cell Biology. The ultimate goal of his findings is to essentially transform medical records into medical knowledge.
  • What's new with H2O Driverless AI? Recorded: Jun 21 2018 48 mins
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    Abstract: H2O Driverless AI empowers data scientists or data analysts to work on projects faster and more efficiently by using automation and state-of-the-art computing power to accomplish tasks that can take humans months in just minutes or hours by delivering automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, time-series, automatic report generation and automatic pipeline generation for model scoring.

    Speaker's Bio:
    Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators.

    Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named “2014 Big Data All-Star” by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.
  • Automatic Visualization with Driverless AI Recorded: May 14 2018 57 mins
    Leland Wilkinson
    We wish to read datasets (text, logs, relational tables, hierarchies, streams, images, ...) and display interesting aspects of their content. The design to do this rests on the grammar of graphics, scagnostics, and a modeler based on the logic of statistical analysis. We distinguish an automatic visualization system (AVS) from an automated visualization system. The former automatically makes decisions about what is to be visualized. The latter is a programming system for automating the production of charts, graphs and visualizations. An AVS is designed to provide a first glance at data before modeling and analysis are done. AVS is designed to protect researchers from ignoring missing data, outliers, miscodes and other anomalies that can violate statistical assumptions or otherwise jeopardize the validity of models. This webinar will cover the theory and operation of the AutoViz implementation of AVS inside Driverless AI.

    Leland's Bio:
    Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He received an A.B. degree from Harvard in 1966, an S.T.B. degree from Harvard Divinity School in 1969, and a Ph.D. from Yale in 1975. Wilkinson wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. After the company grew to 50 employees, he sold SYSTAT to SPSS in 1994 and worked there for ten years on research and development of visualization systems. Wilkinson subsequently worked at Skytree and Tableau before joining H2O.
  • Interpretable Machine Learning Recorded: May 9 2018 62 mins
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    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.

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  • Introduction to H2O.ai, H2O and Driverless AI Recorded: May 1 2018 30 mins
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    Nicholas Png, Partnerships Software Engineer at H2O.ai, will give an overview of H2O, the leading open source machine learning platform for enterprise, which integrates seamlessly with R and Python environments. Nicholas will also discuss Driverless AI, an enterprise automated machine learning solution.

    There will be time at the end of the webinar for attendees to ask questions.

    Nick's Bio:
    Nicholas Png is a Partnerships Software Engineer at H2O.ai. Prior to working at H2O, he worked as a Quality Assurance Software Engineer, developing software automation testing. Nicholas holds a degree in Mechanical Engineering, and has experience working with customers across multiple industries, identifying common problems, and designing robust, automated solutions.
  • Machine Learning Interpretability with Driverless AI Recorded: Apr 25 2018 59 mins
    Patrick Hall, Andy Steinbach
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    In addition to the discussion, Patrick will showcase several approaches beyond the error measures and assessment plots typically used to interpret deep learning and machine learning models and results.

    This will include:

    - Data visualization techniques for representing high-degree interactions and nuanced data structures.
    - Contemporary linear model variants that incorporate machine learning and are appropriate for use in regulated industry.
    - Cutting edge approaches for explaining extremely complex deep learning and machine learning models.

    Wherever possible, interpretability approaches are deconstructed into more basic components suitable for human storytelling: complexity, scope, understanding, and trust.

    Bio:

    Patrick Hall is a Senior Data Scientist and Product Engineer at H2O.ai and works with H2O.ai’s customers to derive substantive business value from machine learning technologies. His product work at H2O.ai focuses on model interpretability and deployment.

    Patrick also is an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and R & D research roles at SAS Institute.

    Andy leads the global effort to develop the NVIDIA deep learning platform in the Financial Services Industry. He specializes in developing applications of revolutionary technologies in new markets. In his last role, he built a new group to employ machine learning technology for the first time in a $1B imaging technology division of the global technology company ZEISS.
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    Megan Kurka, Customer Data Scientist
    Driverless AI speeds up data science workflows by automating feature engineering, model tuning, ensembling, and model deployment.

    Megan Kurka, Customer Data Scientist at H2O.ai, will be answering any questions you have about Driverless AI.

    [Bio]
    Prior to working at H2O, Megan worked as a Data Scientist building products driven by machine learning for B2B customers. Megan holds a degree in Applied Mathematics and has experience working with customers across multiple industries, identifying common problems, and designing robust and automated solutions.
  • An Introduction to H2O4GPU and Driverless AI Recorded: Apr 17 2018 62 mins
    Jon McKinney, Director of Research and Arno Candel, CTO
    H2O.ai Speakers: Jon McKinney, Director of Research and Arno Candel, CTO

    Machine learning has benefited greatly from the recent performance gains of GPUs. H2O.ai’s open source machine learning platform, H2O4GPU, and its award-winning enterprise automated machine learning platform, Driverless AI, are both fully optimized for the latest-generation NVIDIA® architecture.

    In this webinar, you’ll learn about both platforms including:
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    - Benchmark results for common algorithms as compared to running on CPUs
    - Product features
    - Key use cases

    There will be time during the webinar for Q&A.

    About the speakers:
    Jon McKinney is the Director of Research at H2O.ai. He has 20 years of experience building world-class mathematical algorithms and astrophysical models, and executing them on massively parallel systems to explain and predict real-world data.

    Arno Candel is the Chief Technology Officer at H2O.ai. He is the main committer of H2O-3 and Driverless AI and has been designing and implementing high-performance machine-learning algorithms since 2012. Previously, he spent a decade in supercomputing at ETH and SLAC and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He was named "2014 Big Data All-Star" by Fortune Magazine and featured by ETH GLOBE in 2015. Follow him on Twitter: @ArnoCandel.
  • How to Approach a Multi-Class (classification) Problem Recorded: Feb 5 2018 65 mins
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    Ashrith's talk will show how to approach a multi-class (classification) problem using H2O. The data that is being used is an aggregated log of multiple systems that are constantly providing information about their status, connections and traffic. In large organizations, these log datasets can be very huge and unidentifiable due to the number of sources, legacy systems etc. In our example, we use a created response for each source. The use H2O to classify the source of data.

    Ashrith Barthur is a Security Scientist at H2O currently working on algorithms that detect anomalous behaviour in user activities, network traffic, attacks, financial fraud and global money movement. He has a PhD from Purdue University in the field of information security, specialized in Anomalous behaviour in DNS protocol.
  • Driverless AI - Your Expert System for AI Recorded: Feb 1 2018 51 mins
    Arno Candel, Chief Technology Officer at H2O.ai
    H2O.ai’s groundbreaking product, Driverless AI automates machine learning for the enterprise. Driverless AI reduces processing time from days to mere hours and produces accurate and easily interpretable models. AI TO DO AI.

    Take a seat while Arno Candel discusses the benefits of Driverless AI and demonstrates:

    - 40x speedups using GPU enablement
    - Automatic feature engineering to build accurate prediction models
    - Model interpretability and technical reason codes in plain English

    Bio: Arno Candel is the Chief Technology Officer of H2O.ai. He is also the main author of H2O’s Deep Learning. Before joining H2O.ai, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.

    Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine.

    Follow him on Twitter: @ArnoCandel
  • Get Competitive with Driverless AI Recorded: Nov 14 2017 63 mins
    Marios Michailidis
    Recent world #1 Kaggle Grandmaster and Research Data Scientist at H2O.ai, Marios Michailidis, will delve into the competitive edge that Driverless AI brings out of the box.

    Driverless AI can easily score in the top 5% in popular data science challenges against thousands of participants in a matter of minutes with limited processing power.

    Apart from the actual predictions, one can use Driverless AI data munging and derived knowledge of the data to build even more powerful models.

    This webinar discusses how Driverless AI can get competitive scores in popular Kaggle challenges. Also, Marios will explain the concepts of hyper-parameter tuning and stacking and how they help to make stronger predictions.

    Bio:

    Former world no.1 Kaggle Grandmaster, Marios Michailidis, is now a Research Data Scientist at H2O.ai. He is finishing his PhD in machine learning at the University College London (UCL) with a focus on ensemble modeling and his previous education entails a B.Sc in Accounting Finance from the University of Macedonia in Greece and an M.Sc. in Risk Management from the University of Southampton. He has gained exposure in marketing and credit sectors in the UK market and has successfully led multiple analytics’ projects based on a wide array of themes.

    Before H2O.ai, Marios held the position of Senior Personalization Data Scientist at dunnhumby where his main role was to improve existing algorithms, research benefits of advanced machine learning methods, and provide data insights. He created a matrix factorization library in Java along with a demo version of personalized search capability. Prior to dunnhumby, Marios has held positions of importance at iQor, Capita, British Pearl, and Ey-Zein.

    At a personal level, he is the creator and administrator of KazAnova, a freeware GUI for quick credit scoring and data mining which is made absolutely in Java. In addition, he is also the creator of StackNet Meta-Modelling Framework.
  • Driverless AI: Your Expert System for AI Recorded: Jul 27 2017 51 mins
    Arno Candel
    H2O.ai’s groundbreaking product, Driverless AI, is the intelligence of a Kaggle Grandmaster in a box. Driverless AI reduces processing time from days to mere hours and produces accurate and easily interpretable models. AI TO DO AI.

    Take a seat while Arno Candel discusses the benefits of Driverless AI and demonstrates:

    - 40x speedups using GPU enablement
    - Automatic feature engineering to build accurate prediction models
    - Model interpretability and technical reason codes in plain English

    Bio: Arno Candel is the Chief Technology Officer of H2O. He is also the main author of H2O’s Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.

    Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine.

    Follow him on Twitter: @ArnoCandel
Fast, Accurate, Interpretable AI
H2O.ai is the maker of H2O, the world's best machine learning platform and Driverless AI, which automates machine learning. H2O is used by over 130,000 data scientists and more than 13,000 organizations globally. Driverless AI does auto feature engineering and can achieve 40x speed-ups on GPUs.

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  • Live at: May 14 2018 6:00 pm
  • Presented by: Leland Wilkinson
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