Deploying ML Models in Production

This course lays the groundwork for a robust MLOps pipeline by developing core functions that will be reused in subsequent courses. Rather than focusing on the full data science process, learners will implement specific, modular components for data processing, model training, evaluation, and persistence—all critical for later integration in automated retraining and API-based serving.

In this course, learners transition to model serving by integrating their ML model into a web service using FastAPI. The focus is on creating a functional API that leverages the model persistence function from Course 1 and ensures that the prediction endpoint is both robust and secure.