AI isn’t the future of software delivery, it’s the present. It’s reshaping how we build, deploy, and scale systems in real time. While DevOps gave us speed and reliability for traditional applications, it wasn’t designed for the messy, data-driven, unpredictable world of machine learning.
Enter MLOps: the next evolution of DevOps, purpose-built for the age of AI. This talk demystifies how to extend the practices you already know like automation, CI/CD, monitoring, and observability, into the dynamic lifecycle of ML models.
Whether you’re a DevOps engineer staring down your first ML project, a data scientist tired of tossing models over the wall, or a leader accountable for delivering trustworthy AI at scale, you’ll leave with a practical playbook for success and best practices shaped by Grainger’s approach to MLOps.
DevOps transformed how we ship software. Now it’s time to transform how we ship intelligence.