MLOps

[Blog] Build an end-to-end MLOps pipeline using Amazon SageMaker Pipelines, GitHub, and GitHub Actions
[Code] Deep Learning MLOps workshop with Amazon SageMaker
[Code] SageMaker MLOps Multi Account Setup with GitHub and Terraform
[Blog] MLOps foundation roadmap for enterprises with Amazon SageMaker
[Code] SageMaker Projects Repo for MLOps
[Video] Automate MLOps with SageMaker Projects

[Blog] Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines
[Blog] Multi-branch training MLOps pipeline using AWS
[Code] Amazon SageMaker Pipelines Workshop

[Blog] Create Amazon SageMaker projects using third-party source control and Jenkins
[Blog] Build MLOps workflows with Amazon SageMaker projects, GitLab, and GitLab pipelines
[Blog] [External] 5 Simple Steps to MLOps with GitHub Actions, MLflow, and SageMaker Pipelines
[Blog] [External] MLOps with MLFlow and Amazon SageMaker Pipelines
[Blog] [External] Scaling MLOps with resilient pipelines

[Blog] Managing your machine learning lifecycle with MLflow and Amazon SageMaker
[Blog] Improve ML developer productivity with Weights & Biases and Amazon SageMaker

[Blog] Understanding the key capabilities of Amazon SageMaker Feature Store
[Blog] Track your ML experiments with DVC and Amazon SageMaker Experiments
[Blog] Scale ML feature ingestion using Amazon SageMaker Feature Store
[Blog] Extend model lineage to include ML features using Amazon SageMaker Feature Store
[Blog] Control access to Amazon SageMaker Feature Store offline using AWS Lake Formation
[Blog] Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction
[Code] Amazon SageMaker Feature Store Workshop

[Docs] Monitor models for data and model quality, bias, and explainability - Real Time & Batch
[Blog] Retrain your model when data drift is detected

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