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Machine learning model drift & MLOps pipelines | Technically Speaking

Machine learning at the edge is trendy, but what basics do you need to know? In this episode, Kavitha Prasad from Intel joins Chris Wright to talk about machine learning, MLOps and model drift. They discuss causes of model drift, how MLOps is similar to DevOps, how ML pipelines can help make model development and deployment easier, and why machine learning will become more important for models and workloads as edge computing expands. What do you need to know about model drift to build an MLOps pipeline? Join us to learn more about using MLOps practices to scale machine learning using the edge. Learn More: https://red.ht/3KDXjjK https://red.ht/3ABtjQI Follow us: Chris Wright   / kernelcdub   Kavitha Prasad   / kavitha-prasad-2b38737   What is Technically Speaking? Technically Speaking features captivating conversations between Chris Wright and a rotating cast of experts and industry leaders around what's on the horizon for technology. Want to participate? Leave us a comment if there's a topic or a guest you'd like to see featured. Watch More Technically Speaking: https://www.redhat.com/en/technically... Subscribe to Red Hat's YouTube channel: https://www.youtube.com/redhat/?sub_c... #RedHat #MLOps #OpenSource