ML in Action: Deploy Machine Learning Models That Deliver Real-World Impact

Building a great machine learning model is just the beginning-the true value lies in putting it to work. In this final course of the Applied Machine Learning Certificate, you'll learn how to take your machine learning solutions from the lab to the real world by deploying them into production environments where they can drive decisions, power applications, and create measurable results.

Explore practical strategies for integrating machine learning models into live systems, with a focus on implementation methods, deployment workflows, and lifecycle management. You'll examine common challenges-from scaling and latency to versioning and model drift-and discover best practices for maintaining performance and relevance over time.

Using open-source tools and datasets, you'll gain hands-on experience in the last critical step of the machine learning pipeline: making your models production-ready, resilient, and impactful.

All tools and data utilized in this course are open source and freely available for use before, during, and after the course.

What You Will Learn:

• Common methods of implementing machine learning in real-world environments

• How to deploy machine learning models into production

• Challenges that arise during deployment and how to solve them

• Key considerations for managing machine learning solutions throughout their lifecycle

Earn 1.4 Continuing Education Units (CEUs) and walk away ready to launch your models into the world-confident, capable, and production-ready.