Machine Learning in Production

Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset.

The Machine Learning in Production course covers how to conceptualize integrated systems that continuously operate in production as well as solve common challenges unique to the production environment. In striking contrast with standard machine learning modeling, production systems need to handle relentlessly evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance.

In this Machine Learning in Production course, you will build intuition about designing a production ML system end-to-end: project scoping, data needs, modeling strategies, and deployment patterns and technologies. You will learn strategies for addressing common challenges in production like establishing a model baseline, addressing concept drift, and performing error analysis. You’ll follow a framework for developing, deploying, and continuously improving a productionized ML application.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need experience preparing your projects for deployment as well. Machine learning engineering for production combines the foundational concepts of machine learning with the skills and best practices of modern software development necessary to successfully deploy and maintain ML systems in real-world environments.

By the end of this program, you will be ready to:

• Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
• Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.