<description>In this session, we cover best practices for enterprises that want to use powerful open-source technologies to simplify and scale their machine learning (ML) efforts. Learn how to use Apache Spark, the data processing and analytics engine commonly used at enterprises today, for data preparation as it unifies data at massive scale across various sources. We train models using TensorFlow, and we use MLflow to track experiment runs between multiple users within a reproducible environment. We then manage the deployment of models to production. We show you how MLflow can be used with any existing ML library and incrementally incorporated into an existing ML development process. This session is brought to you by AWS partner, Databricks.</description>

AWS re:Invent 2018

AWS

AIM396: ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle

DEC 1, 201849 MIN
AWS re:Invent 2018

AIM396: ML Best Practices: Prepare Data, Build Models, and Manage Lifecycle

DEC 1, 201849 MIN

Description

In this session, we cover best practices for enterprises that want to use powerful open-source technologies to simplify and scale their machine learning (ML) efforts. Learn how to use Apache Spark, the data processing and analytics engine commonly used at enterprises today, for data preparation as it unifies data at massive scale across various sources. We train models using TensorFlow, and we use MLflow to track experiment runs between multiple users within a reproducible environment. We then manage the deployment of models to production. We show you how MLflow can be used with any existing ML library and incrementally incorporated into an existing ML development process. This session is brought to you by AWS partner, Databricks.