Gradient to Production: MLOps & Model Serving

About this course

Most machine learning practitioners know how to build models. Far fewer know how to ship them reliably, maintain them over time, and operate the systems that surround them. This program closes that gap. Gradient to Production is a comprehensive, intermediate-level program designed for data scientists, ML engineers, and analytics engineers who are ready to move beyond the notebook and into production. Across 15 focused courses, you will build the full stack of MLOps skills that modern AI teams require: designing resilient data pipelines, engineering reusable Python packages, deploying and containerizing models, serving inference APIs, testing ML systems rigorously, monitoring for drift, and documenting your work so teams can trust and build on it. You will work with tools and frameworks used across the industry, including FastAPI, Docker, Kubernetes, Apache Airflow, scikit-learn, GitHub Actions, and pytest. Every course combines concise instruction with hands-on labs, guided coaching, and realistic workflows that reflect how production ML teams actually operate. By the end of the program, you will be equipped to design, deploy, test, monitor, and maintain ML systems end-to-end — with the engineering discipline, operational judgment, and communication skills that distinguish practitioners who experiment from engineers who deliver.

Course objectives

• equip learners with end-to-end ML system design and deployment skills
• develop engineering discipline and operational judgment for ML projects
• foster communication skills for effective teamwork in ML environments