AWS: Workflow in SageMaker

An article to alleviate your confusion forever

Rahul S
3 min readOct 7, 2023

AWS SageMaker is a fully-managed service for machine learning in the cloud. It lets us build and train ML models, and directly deploy them into a production-ready, hosted environment. Its various features include:

  • An integrated Jupyter notebook authoring instance. It offers easy access to data sources for exploration and analysis. There is no need to manage servers.
  • Common machine learning algorithms optimized to run against large data in a distributed environment. SM also lets you use your own frameworks and algorithms, offering flexible distributed training capabilities that can adjust to specific workflows.

I prefer SM console over Studio

WORK FLOW

In SM, ML modeling process comprises 1. preparation, 2. training, and 3. deployment. Following diagram illustrates typical workflow.

src: AWS

1. PREPARATION

SageMaker creates a fully-managed ML instance in Amazon EC2 and runs Jupyter computational processing notebooks.

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