Architecture Best Practices for Machine Learning

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Featured topic: ML Operations (MLOps)

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AWS Well-Architected ML Lens

We provide a set of established cloud and technology agnostic best practices and guidance for machine learning workloads.

Category Deep Dive: Machine Learning

Learn how to add intelligence to your applications, and quickly build, train, and deploy machine learning models at scale.

Why Should You Use MLOps?

We explain how the discipline of ML Operations (MLOps) can help you use AI/ML to transform your business at scale.
Let’s Architect! Architecting for Machine Learning

We teach you how to build it with AWS CDK, architect your system for MLOps, and automate the deployment of the solutions for A/B testing.

MLOps Foundation Roadmap for Enterprises with Amazon SageMaker

We show you the key phases of building an MLOps foundation, how multiple personas work together on this foundation, and our purpose-built tools.

Build a Secure Enterprise Machine Learning Platform on AWS

We provide architecture patterns, code samples, and best practices for building an enterprise ML platform on AWS.
Automate MLOps with SageMaker Projects

We show you how to automate MLOps with Amazon SageMaker Projects to help you standardize governance practices in ML projects.

Automate Machine Learning Workflows

We walk you through how to create and automate end-to-end machine learning workflows using Amazon SageMaker.

SageMaker MLOps Project Walkthrough

We demonstrate using Amazon SageMaker MLOps projects to create a CI/CD system to build, train, and deploy models.
MLOps Workload Orchestrator

Deploy a solution that helps you streamline and enforce architecture best practices for machine learning model productionization.

Deploy an MLOps Solution That Hosts Your Model Endpoints in AWS Lambda

Launch a solution that is a fully CI/CD-driven approach to machine learning that you can adapt for your use cases.

Improving Forecast Accuracy with Machine Learning

Deploy a solution that produces forecasts and visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks.
Optimize Your ML Deployments with Auto Scaling on Amazon SageMaker

We provide a design pattern for deriving the right auto scaling configuration for your ML-based applications.

Monitor Models for Data and Model Quality, Bias, and Explainability

Learn how to set up continuous monitoring of the quality of Amazon SageMaker machine learning models in production.

Build a Cross-Account MLOps Workflow Using the SageMaker Model Registry

We discuss Amazon SageMaker cross-account features that allow you to share and manage model groups as well as manage model versions.

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