Building ML best practices with MLOps

MLOps, or Machine Learning Operations, are the practices and tools used to manage the full lifecycle of machine learning models, from development to deployment and maintenance. The goal of MLOps is to make deploying machine learning models to production as seamless and efficient as possible.

Managing a machine learning application in production requires a robust MLOps pipeline to ensure that the model is continuously updated and relevant as new data becomes available. MLOps help automate the building, testing, and deploying machine learning models. It manages the data and resources used to train and evaluate models. Apply mechanisms to monitor and maintain deployed models to detect and address drift, data quality issues, and bias. Finally enable collaboration and communication between data scientists, engineers, and other stakeholders.

The first step in implementing MLOps in AWS is clearly defining the machine learning workflow, including the data...