Machine Learning Lens - Machine Learning Lens

Machine Learning Lens

Publication date: October 12, 2021 (Document history and contributors)

Machine learning (ML) algorithms discover and learn patterns in data, and construct mathematical models to enable predictions on future data. These solutions can revolutionize lives through better diagnosis of diseases, environment protection, products and services transformation, and more.

This whitepaper provides you with a set of established cloud and technology agnostic best practices. You can apply this guidance and architectural principles when designing your ML workloads, or after your workloads have entered production as part of continuous improvement. The paper includes guidance and resources to help you implement these best practices on AWS.


The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS. By using the Framework, you will learn operational and architectural best practices for designing and operating workloads in the cloud. It provides a way to consistently measure your operations and architectures against best practices and identify areas for improvement.

Your ML models depend on the quality of input data to generate accurate results. As data changes with time, monitoring is required to continuously detect, correct, and mitigate issues with accuracy and performance. This may even require you to retrain your model with the latest refined data.

Application workloads rely on step-by-step instructions to solve a problem. ML workloads enable algorithms to learn from data through an iterative and continuous cycle. The ML lens complements and builds upon the Well-Architected Framework to address this difference between these two types of workloads.

This paper is intended for those in a technology role, such as chief technology oļ¬ƒcers (CTOs), architects, developers, data scientists, and ML engineers. After reading this paper, you will understand the best practices and strategies to use when you design and operate ML workloads on AWS.