Building ML best practices with MLOps
MLOps are the practices and tools used to manage the full lifecycle of ML models, from development to deployment and maintenance. The goal of MLOps is to make deploying ML models to production as seamless and efficient as possible.
Managing an ML application in production requires a robust MLOps pipeline to ensure that the model is continuously updated and relevant as new data becomes available. MLOps helps automate the building, testing, and deploying of ML 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, and finally enables communication and collaboration between data scientists, engineers, and other stakeholders.
The first step in implementing MLOps in AWS is clearly defining the ML workflow, including the data ingestion, pre-processing, model training, and deployment stages. The following...