Part 2: What is MLOps?

This is a podcast episode titled, Part 2: What is MLOps?. The summary for this episode is: <p>This episode of&nbsp;<em>Techsplainers</em>&nbsp;explores the practical implementation of MLOps, diving into the key components that comprise an effective machine learning operations pipeline. We examine the five essential elements: data management (including acquisition, preprocessing, and versioning), model development (covering training, experimentation, and evaluation), model deployment (focusing on packaging and serving), monitoring and optimization (highlighting performance tracking and retraining), and collaboration and governance (emphasizing version control and ethical guidelines). The podcast also investigates how generative AI and large language models are reshaping MLOps practices before explaining the four maturity levels of MLOps implementation—from manual processes to fully automated systems with continuous monitoring and governance. Throughout the episode, we emphasize that organizations should select the appropriate MLOps maturity level based on their specific needs rather than pursuing the most advanced level by default. </p><p><br></p><p>Find more information at&nbsp;<a href="https://www.ibm.com/think/podcasts/techsplainers" rel="noopener noreferrer" target="_blank">https://www.ibm.com/think/podcasts/techsplainers</a> </p><p><br></p><p><strong>Narrated by Ian Smalley</strong></p>