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Tutorials

The H2O eScorer tutorials provide a step-by-step guideline for you to try out the eScorer features. These two tutorials showcase how you can use the eScorer Python client to make predictions on data, in real time or as a batch.

Learning path​

  • Tutorial 1A: Realtime scoring with the Python client

    This tutorial describes how to use the H2O eScorer Python client to make predictions on data in real-time. The client is light-weight and concurrent, so you can score tens of thousands of rows in less than ten lines of code. Deploying machine learning models on H2O eScorer is easy with the drag-and-drop upload option in the Wave app.

  • Tutorial 1B: Batch scoring with the Python client

    This tutorial showcases the use of H2O eScorer Python client to run the Batch Scorer. Batch scoring is built to read, score and write large amounts of datasets from storage. An example of a storage is AWS S3. In this tutorial, we will use a "properties" file with the eScorer Python client to score a ".csv" dataset from an AWS S3 bucket, and write results back to the same bucket.

  • Tutorial 2A: Snowflake integration and Snowpark Container Services

    This tutorial guides you through the integration of H2O eScorer with Snowflake and Snowpark Container Services (SPCS).

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