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Deploying Machine Learning Models in Production via Coursera


In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to minimize model decay and performance drops; and apply progressive delivery techniques to maintain and monitor a continuously operating production system.

Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.


  • Week 1: Model Serving: Introduction
    • Learn how to make your ML model available to end-users and optimize the inference process.
  • Week 2: Model Serving: Patterns and Infrastructure
    • Learn how to serve models and deliver batch and real-time inference results by building scalable and reliable infrastructure.
  • Week 3: Model Management and Delivery
    • Learn how to implement ML processes, pipelines, and workflow automation that adhere to modern MLOps practices, which will allow you to manage and audit your projects during their entire lifecycle.

Taught by

Laurence Moroney and Robert Crowe

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