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H2O.ai

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  • Catch-up with the latest in Driverless AI
    Catch-up with the latest in Driverless AI Arno Candel Recorded: Sep 18 2018 65 mins
    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!
    Meet Katanna – Does the work of thousands, runs 24/7 and never takes a vacation! Bharath Sudharsan Recorded: Aug 28 2018 49 mins
    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
    Scalable Machine Learning on KubeFlow with H2O.ai Nick Png Recorded: Aug 21 2018 47 mins
    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
    Peek Under the Hood of H2O Driverless AI with Auto Doc Megan Kurka, Vinod Iyengar Recorded: Aug 15 2018 60 mins
    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
    Productionizing H2O Models with Apache Spark Jakub Hava Recorded: Aug 2 2018 46 mins
    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
    Time-Series in Driverless AI Marios Michailidis Recorded: Jul 25 2018 45 mins
    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
    Machine Learning in Reproductive Science: Human Embryo Selection and Beyond Oleksii Barash Recorded: Jul 16 2018 42 mins
    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?
    What's new with H2O Driverless AI? Arno Candel Recorded: Jun 21 2018 48 mins
    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
    Automatic Visualization with Driverless AI Leland Wilkinson Recorded: May 14 2018 57 mins
    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
    Interpretable Machine Learning Patrick Hall Recorded: May 9 2018 62 mins
    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!

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