Summary

The journey of a DataOps or MLOps engineer is just a DevOps engineer who has gotten some understanding of data and machine learning concepts. That’s pretty much it. But, as we saw in this chapter, the usage of those concepts is a pretty useful thing.

First, we talked about the differences and similarities between DevOps and these associated fields and how they are connected with each other. Using that, we managed to produce a couple of practical use cases that can come in handy when using Python with DataOps and MLOps.

Next, we talked about handling the proverbial big data. We talked about the aspects that make the data so big and how to tackle each of these aspects individually using a use case for each.

Finally, we talked about ChatGPT and how it works in delivering all the things that it delivers to users around the world. We discussed the simplicity of its complexity and its mystery, as well as the new age of open source LLMs that has accelerated the development...