Why MLOps is needed for deploying large-scale ML workloads

The most important aspect of MLOps is that it helps organizations develop ML models in a faster, more efficient, and reliable manner, and it allows data science teams to experiment and innovate while also meeting operational requirements.

We know by now that ML has become an essential component of many industries and sectors, providing invaluable insights and decision-making capabilities, but that deploying ML models, especially at scale, presents many challenges. Some of these are challenges that can only be solved by MLOps, and we dive into more detail on such challenges in this section, as well as providing examples of how MLOps helps to address them.

Before we dive in, I’m going to point out that the kinds of challenges we will discuss in this section actually apply to any industry that creates products at a large scale, whether those products are cars, safety pins, toys, or machine learning models.

I’...