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Amazon SageMaker: Machine Learning Workflows

Welcome to QA’s lesson where you’re going to learn about Machine Learning Operations or MLOPs with Amazon SageMaker’s fully managed workflow service.

By the end of this lesson, you will have an understanding of Amazon SageMaker workflow technologies including:

  • MLOps and its advantages, specifically how it integrates with Amazon SageMaker.
  • Amazon SageMaker Studio, highlighting the functionality of its Integrated Development Environment in utilizing key features of Amazon SageMaker.
  • The roles of SageMaker Debugger and Model Monitor in improving the management of the machine learning lifecycle.
  • The functionality of Amazon SageMaker Projects in assisting organizations to develop CI/CD practices for MLOps engineers.
  • A range of tools for building and managing machine learning pipelines, including SageMaker Model Building Pipelines, SageMaker Operators for Kubernetes, and SageMaker Components for Kubeflow Pipelines.

Intended Audience

Data Scientists and Machine Learning engineers, specifically those who are interested in MLOps and Amazon SageMaker.

Prerequisites

To get the most out of this lesson you should have an understanding of MLOps, Amazon SageMaker, and familiarity with Machine Learning terms.

Get Started

Amazon SageMaker: Machine Learning Workflows Lesson

This post is licensed under CC BY 4.0 by the author.