MMI712 MACHINE LEARNING SYSTEMS DESIGN AND DEPLOYMENT
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Course ContentThe course covers several aspects of designing reliable and scalable machine learning systems for real-world deployment. It deals with development of production quality models and introduces the machine learning pipeline, concepts on machine learning system design and data engineering. It provides know-how on model development, and how to scale up the training for large models as well as evaluation, calibration and debugging of these models. Generation of reproducible models via experiment tracking tools and model versioning is also covered. Hardware platforms and frameworks for deployment are introduced, followed by basic deployment concepts, containerized deployment and testing.
The course aims to balance theory and practice. The lab sessions allow students to experience the concepts first-hand and cover deployment (Model Development and Training, Model, Data, Parallelism, Experiment Tracking) and deployment aspects (Model Compression and Optimization, Basic Deployment, Containerized Deployment, Testing in Production).