What is MLOps, and why do we need it?
Probably one of the most difficult tasks in machine learning is the leap from proof of concept to production-ready application. Meaning, an ML model that performs brilliantly under experiments often fails if applied in real-world scenarios. Only 32% of data scientists surveyed say their ML models usually deploy. The pervasive failure of AI/ML projects comes mainly from the lack of a structured framework and standardized processes that can help with the shift.
This is where machine learning operations, or MLOps, come in handy.
