Enterprise MLOps 101

The boom in AI has seen a rising demand for better AI infrastructure — both in the compute hardware layer and AI framework optimizations that make optimal use of accelerated compute. Unfortunately, organizations often overlook the critical importance of a middle tier: infrastructure software that standardizes the machine learning (ML) life cycle, adding a common platform for teams of data scientists and researchers to standardize their approach and eliminate distracting DevOps work. This process of building the ML life cycle is known as MLOps, with end-to-end platforms being built to automate and standardize repeatable manual processes. Although dozens of MLOps solutions exist, adopting them can be confusing and cumbersome. What should you consider when employing MLOps? How can you build a robust MLOps practice? Join us as we dive into this emerging, exciting, and critically important space.