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H2O.ai

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  • AI Improves Profitability of Core Financial Services at Paraguay Bank
    AI Improves Profitability of Core Financial Services at Paraguay Bank
    Ruben Diaz, Data Scientist at Vision Banco and Rafael Coss, Maker at H2O.ai Recorded: Mar 20 2019 60 mins
    In the financial industry, data can translate to revenue if used correctly, yet financial institutions need to operate with scale, speed, and immense accuracy.

    Data scientists at Visión Banco needed to improve the bank’s credit scoring process, including predicting existing customer behavior and churn, determining credit risk, and offering credit to new customers. Join our webinar to learn how the bank saved time and improved accuracy by building and deploying models using H2O Driverless AI. As a result, the Paraguayan bank has doubled its rate of customer propensity to buy.

    Join our webinar to learn:

    • How to automate machine learning modeling to create more models faster and scale data science efforts
    • How you can use high-performance computing to solve complex data challenges such as real-time targeting of promotions or customer churn predictions
    • How one financial institution now easily determines credit risks and expands offers to customers using H2O Driverless AI
    • How you can optimize business processes across your financial institution, such as evaluating credit scores or credit risk, detecting fraud, or performing analysis for Know Your Customer (KYC)
  • Considerations for Deploying AI and Machine Learning in the Cloud
    Considerations for Deploying AI and Machine Learning in the Cloud
    Vinod Iyengar, H2O.ai Recorded: Mar 12 2019 62 mins
    As the world is moving towards cloud deployments, enterprises of all sizes are trying to figure out the best ways to optimize their workloads using the available set of resources. This often involves evaluating their portfolio of workloads and applications and identifying the best cloud or non-cloud venue to host each.

    The decision process is based on multiple considerations, including performance, integration issues, economics, competitive differentiation, solution maturity, risk tolerance, regulatory compliance considerations, skills availability, and partner landscape.

    We'll talk about all the above and some practical ideas on how to go about such a journey specifically from an AI and ML perspective. Finally, we'll also look at a few example deployments with H2O, Sparkling Water, and Driverless AI.
  • Get Your Feet Wet with H2O Driverless AI
    Get Your Feet Wet with H2O Driverless AI
    Nicholas Png Recorded: Feb 27 2019 53 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:

    Deep Learning Tensorflow Models
    Standalone Java Scoring Pipeline
    Deep Learning for NLP/Text
    LightGBM Models
    Interpretability for Time-Series Capability
    Advanced Feature Ensemble
    Local Feature Brain
    FTRL Models, Model Diagnostics, Model Retraining


    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.
  • Explaining Explainable AI
    Explaining Explainable AI
    Patrick Hall from H2O.ai and Tom Aliff from Equifax Recorded: Jan 30 2019 46 mins
    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
    Automated Machine Learning for Data Practitioners and BI Analysts
    Karthik Guruswamy Recorded: Jan 8 2019 58 mins
    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
    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.

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