MLOps practices help accelerate and streamline the ML development lifecycle. ML engineers, join us for a hands-on demo showing how to use Amazon SageMaker to implement MLOps practices, including automating ML workflows, building CI/CD pipelines for ML, monitoring models in production, and standardizing model governance.
Learning Objectives:
* Objective 1: Explore how to automate ML workflows to accelerate data preparation and model building, training, and experiments.
* Objective 2: Learn how to build continuous integration and delivery (CI/CD) pipelines to reduce model management overhead.
* Objective 3: Find out how to monitor quality of ML models by automatically detecting bias, model drift, and concept drift.
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