ML platform monitoring: Best practices

Machine learning (ML) platforms such as Amazon Sagemaker, Azure Machine Learning, and Google Vertex AI are fully managed services that enable data scientists and engineers to easily build, train, and deploy ML models. Common use cases for ML platforms include natural language processing (NLP) models for text analysis and chatbots, personalized recommendation systems for e-commerce web applications and streaming services, and predictive business analytics. These platforms help simplify and expedite each step of the ML workflow by providing a broad set of tools that allow you to automate and scale ML development tasks—from preprocessing data to training and deploying models—on a single platform. Monitoring the availability and efficient utilization of cloud resources is crucial for ensuring that your managed platform can support your ML workloads, as insufficient resources can result in prolonged training times, reduced freshness of your models, and slow inference speeds. This negatively impacts performance and leads to inaccurate predictions, diminishing customer satisfaction and hurting your bottom line. In this post, we will go over a typical ML workflow using managed platforms and some monitoring best practices that are important at each step involved in producing and maintaining ML models. Then we’ll look at key rate, error, and duration (RED) metrics to monitor to ensure your ML-powered applications maintain peak performance.