Will MLOps practitioners move towards all-in-one platforms like DevOps did with GitLab and why were point solutions originally used?

It all happened very quickly. ML started taking off just five or six years ago, and all these point solutions started coming up. Aquarium came out five years ago, for example. It's the same thing as Scale’s Nucleus product. They try to solve the piece where you have billions of data points. Which ones should you send for annotation? Which ones are at the corner cases? How do I find more data that is similar to the corner cases? This is just one example. 

People realized that “Okay, machine learning is starting to work, we need to really prioritize it." Then, all these companies started cropping out to help with pieces of the puzzle. For instance, Weights and Biases helped with monitoring the models.

All these companies were built to solve a specific problem for the ML teams, and then, it was left to the ML teams internally to piece this all together to make something cohesive so they could train and practice things. 

The next step in MLOps is what happened in DevOps with these toolchains merging into one because it's really just one thing that they're doing, different aspects of the same thing, and it's best provided in a single package.