Machine Learning Operations (MLOps) is a fast-growing domain the field of AI. As more models are deployed in production, the need for a structured, agile, end-to-end ML lifecycle with automation has grown multifold. MLOps provides structure to machine learning projects and help them succeed over the long run. In this course, instructor Kumaran Ponnambalam focuses on the key concepts of MLOps and helps you apply these concepts to your day-to-day ML work. Kumaran introduces you to the machine learning life cycle and explains unique challenges with ML, as well as important definitions and principles. He walks you through the requirements and design for ML projects, then dives into data processing and management. Kumaran explains various tools and technologies that you can use in the automation and management of continuous training. He covers best practices for model management, then offers detailed instruction on continuous integration.



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