This episode of Techsplainers introduces MLOps (machine learning operations), a methodology that creates an efficient assembly line for building and running machine learning models. The podcast explains how MLOps evolved from DevOps principles to address the unique challenges of ML development, including resource intensity, time consumption, and siloed teams. We explore the key benefits of MLOps—increased efficiency through automation, improved model accuracy through continuous monitoring, faster time to market, and enhanced scalability and governance. The episode details eight core principles that define effective MLOps practices: collaboration, continuous improvement, automation, reproducibility, versioning, monitoring and observability, governance and security, and scalability. Finally, we examine the key elements of successful MLOps implementation, including the necessary technical and soft skills, essential tools like ML frameworks and CI/CD pipelines, and best practices for model lifecycle management.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Ian Smalley