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H2O.ai

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  • Distributed Machine Learning with H2O on Containers
    Distributed Machine Learning with H2O on Containers
    Vinod Iyengar, Sr. Director, Alliances, H2O.ai; Nanda Vijaydev, Sr. Director, Solutions, BlueData Recorded: Dec 13 2018 59 mins
    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
    Tackling the Top 4 Challenges to Getting AI into Your Enterprise
    Scott Soutter and Vinod Iyengar Recorded: Dec 5 2018 59 mins
    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
    Enabling Data Connectors in H2O Driverless AI
    Nicholas Png Recorded: Nov 27 2018 37 mins
    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
    Natural Language Processing (NLP) with Driverless AI
    SRK Recorded: Oct 2 2018 61 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.

    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
    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.

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