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An Introduction to Machine Learning Models in Production

This course lays out the common architecture, infrastructure, and theoretical considerations for managing an enterprise machine learning (ML) model pipeline. Because automation is the key to effective operations, you'll learn about open source tools like Spark, Hive, ModelDB, and Docker and how they're used to bridge the gap between individual models and a reproducible pipeline. You'll also learn how effective data teams operate; why they use a common process for building, training, deploying, and maintaining ML models; and how they're able to seamlessly push models into production. The course is designed for the data engineer transitioning to the cloud and for the data scientist ready to use model deployment pipelines that are reproducible and automated. Learners should have basic familiarity with: cloud platforms like Amazon Web Services; Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; Bash, Docker, and REST.

Understand how to set-up and manage an enterprise ML model pipeline
Learn the common components that make up enterprise ML model pipelines
Explore the use and purpose of pipeline tools like Spark, Hive, ModelDB, and Docker
Discover the gaps in the Spark ecosystem for maintaining and deploying ML pipelines
Learn how to move from creating one-off models to building a reproducible automated pipeline

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Tags: Introduction, Machine, Learning, Models, Production

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