In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running.
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Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times.
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.
Course 4 of 4 in the Machine Learning Engineering for Production (MLOps) Specialization.
Syllabus
WEEK 1
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 2
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.
WEEK 3
Model Monitoring and Logging
Establish procedures to detect model decay and prevent reduced accuracy in a continuously operating production system.
