Getting Started with MLOps: ML models and Amazon Sagemaker

A machine learning model is more than just training an algorithm on data.

Once a model is trained, how do we deploy it, make predictions, monitor its performance, and update it when needed?

This is where MLOps comes in:

• Machine Learning: Building and training models using data.
• MLOps: Managing the ML lifecycle, from expermientation and training to deployment and monitoring
• Amazon Sagemaker: Amazon Sagemaker is an AWS machine learning service that provides tools and infrastructure to build, train, deploy, and manage ML models

A simple way to understand the workflow:

Data -> Model training -> Model Evaluation -> Deployment -> Monitoring

I am starting by exploring the fundamental of MLOps and how Amazon Sagemaker helps take ML models from experimentation to production.