The AWS Well-Architected Framework helps organizations understand the benefits and risks associated with their architectural decisions when building workloads on AWS. It provides operational and architectural best practices, enabling users to measure their operations against these standards and identify areas for improvement.

The Machine Learning Lens enhances the AWS Well-Architected Framework by addressing the unique characteristics of machine learning workloads, which rely on algorithms learning from data. It provides best practices and strategies specifically tailored for designing and operating ML workloads on AWS.

Monitoring is crucial in the machine learning lifecycle because the accuracy of ML models depends on the quality of input data, which can change over time. Continuous monitoring allows organizations to detect, correct, and mitigate issues with model accuracy and performance, often necessitating model retraining with the latest refined data.