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Explaining Explainable AI

In this webinar, we will conduct a panel discussion with Patrick Hall and Tom Aliff around the business requirements of explainable AI and the subsequent value that can benefit any organization.
Recorded Jan 30 2019 46 mins
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Presented by
Patrick Hall from H2O.ai and Tom Aliff from Equifax
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  • Explaining Explainable AI Recorded: Jan 30 2019 46 mins
    Patrick Hall from H2O.ai and Tom Aliff from Equifax
    In this webinar, we will conduct a panel discussion with Patrick Hall and Tom Aliff around the business requirements of explainable AI and the subsequent value that can benefit any organization.
  • Automated Machine Learning for Data Practitioners and BI Analysts Recorded: Jan 8 2019 58 mins
    Karthik Guruswamy
    This webinar gently introduces H2O Driverless AI tool to Data Scientists at all levels. BI Analysts who are on the path to be a Data Scientist would also find this tool very useful. Discussion of a business problem will be followed by a quick demo. Without writing a single line of code, we will build a production deployable AI model. Learn things like choosing a Target Variable, a Scorer, and also how to play with the Accuracy, Time and Interpretability to build a model. The webinar will also explore on how to interpret complex non-linear models with simple visuals that can be used to communicate to a business or regulators easily.

    About Karthik Guruswamy:

    Karthik is a “business first” data scientist. His expertise and passion have always been around building game-changing solutions - by using an eclectic combination of algorithms, drawn from different domains. He has published 50+ blogs on “all things data science” in Linked-in, Forbes and Medium publishing platforms over the years for the business audience and speaks in vendor data science conferences. He also holds multiple patents around Desktop Virtualization, Ad networks and was a co-founding member of two startups in silicon valley.
  • Distributed Machine Learning with H2O on Containers Recorded: Dec 13 2018 59 mins
    Vinod Iyengar, Sr. Director, Alliances, H2O.ai; Nanda Vijaydev, Sr. Director, Solutions, BlueData
    Join this webinar to learn about deploying H2O in large-scale distributed environments using containers.

    Artificial intelligence and machine learning are now a top priority for most enterprises. But it can be challenging to implement multi-node AI / ML environments for data science teams in large-scale enterprise deployments.

    Together, BlueData and H2O.ai deliver a game-changing solution for AI / ML in the enterprise. In this webinar, discover how you can:

    -Quickly spin up containerized H2O and Driverless AI environments whether on dev/test or production
    -Ensure seamless support for H2O running on CPUs or GPUs, and provide a secure connection to your data lake
    -Operationalize your distributed machine learning pipelines and deliver faster time-to-value for your AI initiative

    Find out how to run AI / ML on containers while ensuring enterprise-grade security, performance, and scalability.
  • Tackling the Top 4 Challenges to Getting AI into Your Enterprise Recorded: Dec 5 2018 59 mins
    Scott Soutter and Vinod Iyengar
    About this webinar:
    Every enterprise of every size and in every industry can benefit from AI. Many enterprises confront some fundamental challenges, however, that involve their data, security, the data science talent gap, and even cultural transformations that get in their way of deploying AI in their business. In this webinar, AI industry experts from IBM and H2O.ai discuss in a frank and open conversation the top challenges and how they can be addressed in a reasonable manner. Join this webinar to hear what these challenges are and how they can be overcome. Come with your most pressing AI questions and get answers that are actionable today.

    Speakers:
    Scott Soutter, AI Expert in IBM Cognitive Systems
    Vinod Iyengar, Leader of Data Science Transformations at H2O.ai
  • Enabling Data Connectors in H2O Driverless AI Recorded: Nov 27 2018 37 mins
    Nicholas Png
    Enabling Data Connectors in H2O Driverless AI

    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 provides various data connectors for external data sources. Data sources are exposed in the form of the file systems. Each file system is prefixed by a unique prefix.
    We will go over various data connectors available in H2O Driverless AI and how to use them with docker image and native installs. Following are a few connectors to learn of:

    Using Data Connectors with the Docker Image

    o HDFS
    o S3
    o Google Cloud Storage
    o Google BigQuery
    o kdb+
    o Minio
    o Snowflake

    • Using Data Connectors with Native Installs

    o HDFS
    o S3
    o Google Cloud Storage
    o Google Big Query
    o kdb+
    o Minio
    o Snowflake
  • Natural Language Processing (NLP) with Driverless AI Recorded: Oct 2 2018 61 mins
    SRK
    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.

    In the latest version of our Driverless AI platform, we have included Natural Language Processing (NLP) recipes for text classification and regression problems. With this new capability, Driverless AI can now address a whole new set of problems in the text space like automatic document classification, sentiment analysis, emotion detection and so on using the textual data. Stay tuned to the webinar to know more.
  • Catch-up with the latest in Driverless AI Recorded: Sep 18 2018 65 mins
    Arno Candel
    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.

    We will be discussing the latest in Driverless AI, as follows:

    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.

    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.
  • Meet Katanna – Does the work of thousands, runs 24/7 and never takes a vacation! Recorded: Aug 28 2018 49 mins
    Bharath Sudharsan
    Abstract:
    In this talk, you will see how H2O machine learning was used to bridge the gap between a human-based service and a scalable physician-patient matching specialist recommendation platform designed to help thousands of patients per month. The initial healthcare navigation experts, (Kathy & Johanna), have decades of navigation experience recommending specialists. H2O machine learning was applied to merge their experienced thought processes into a resulting platform known as “Katanna”. The result is a scalable, automated, data-driven approach that handles the high volume needs of a healthcare physician recommendation service based on sophisticated ontologies and algorithms that dissect provider quality at the subspecialty and condition level.

    Speaker Bio:

    1. Bharath Sudharsan is the Director of Data Science and Innovation at ArmadaHealth. He leads a team of data analysts who develop and implement AI tools that are at the heart of objective and data-driven specialty care referral process synonymous with ArmadaHealth. Bharath has also held positions at Fractal Analytics and Quanttus, Inc. and WellDoc, Inc. He is also the founder of Geetha, LLC, a provider of best in class healthcare analytics consultation including implementation of NLP and AI.

    2. Ryan Kosiba is Senior Associate with the Data Science & Innovation team at ArmadaHealth. His role requires the application of logic, research and best practices to sustain the integrity of the underlying system for our data health solutions. This involves data cleansing, data validation and statistical analysis. Ryan is also responsible for defining and managing rules-based systems, as well as supporting various internal and external data science projects.
    Prior to his role as Senior Associate, Ryan served as Senior Research & Credentialing Analyst with ArmadaHealth. He was responsible for creating customized reports and utilizing analytics to evaluate research and data integrity.
  • Scalable Machine Learning on KubeFlow with H2O.ai Recorded: Aug 21 2018 47 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
    Arno Candel
    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
    Patrick Hall
    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!
  • Introduction to H2O.ai, H2O and Driverless AI Recorded: May 1 2018 30 mins
    Nicholas Png
    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
    Join us for this webinar with Andy Steinbach, Head of AI in Financial Services at NVIDIA, as he moderates a discussion with Patrick Hall, Senior Data Scientist and Product Engineer at H2O.ai on Machine Learning Interpretability with Driverless AI.

    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.
  • Driverless AI Q&A with Megan Kurka, Customer Data Scientist Recorded: Apr 17 2018 31 mins
    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:
    - How H2O4GPU and Driverless AI are optimized for Volta and CUDA 9
    - 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.
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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  • Presented by: Patrick Hall from H2O.ai and Tom Aliff from Equifax
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