From the course: Amazon SageMaker for Generative AI Applications
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Integrate MLOps with SageMaker - Amazon SageMaker Tutorial
From the course: Amazon SageMaker for Generative AI Applications
Integrate MLOps with SageMaker
- [Narrator] We just covered the what and why of MLOps. Now let's talk about the how did you know that you can integrate MLOps practices directly into your Amazon SageMaker workflows to make your machine learning lifecycle smoother, faster, and more reliable. SageMaker is more than just a model training tool. It offers a full suite of features that support MLOps end to end. From data prep and model experimentation to CI/CD, monitoring and governance. By integrating MLOps into SageMaker, you reduce manual steps, make your workflows reproducible, and ensure your models are production ready from day one. Let's look at the key components. SageMaker Pipelines, a fully managed workflow tool to automate steps like pre-processing, training, evaluation, and deployment. Think of it as CI/CD for ML. Model registry, a central place to version, approve and track models. You can manage which models are ready for staging versus production. SageMaker projects, pre-built MLOps templates that use code…
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