Bringing DevOps and Automation to Machine Learning via MLOps
The vast majority of organizations are new to AI/ML. As a result, most in-house systems and processes supporting this is likely ad-hoc. Industry analysts like Gartner forecast that organizations will need to quickly transition from Pilots to Production with AI/ML in order to make it across the chasm.
Most organizations already have reasonably mature DevOps processes and systems in place. So, going mainstream with AI should be a walk in the park. Correct? Turns out that this is not really true “IT leaders responsible for AI are discovering the AI pilot paradox, where launching pilots is deceptively easy but deploying them into production is notoriously challenging.” by Chirag Dekate, Gartner
In this blog, we will try and answer the following question:
Why do we need a new process called MLOps when most organizations already have reasonably mature DevOps practices? How is MLOps different from DevOps?
