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Mastering MLOps: Building Future-Ready ML Systems
Originally aired:
About the Session
Doing ML has become ubiquitous, and businesses worldwide are transitioning their data workloads to ML workloads. In this session, we will explore the operations side of machine learning, commonly referred to as MLOps (DevOps equivalent for ML). Just doing an ML model isn't enough anymore, and data science teams are expected to build robust, battle-tested, scalable, and autonomous systems. We will, in this session, explore Google's three MLOPs maturity stages and explore concepts like Model & Data Pipelines, Continuous Integration (CI), Deployment(CD), and Monitoring (CM) of ML artifacts to build future-ready ML systems.
Mastering MLOps: Building Future-Ready ML Systems
Originally aired:
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