Published on March 17, 2022
Issues with reproducibility appeared. Conda did help some. Our objective is building our own pipeline infrastructure with an eye out for reproducibility issues, enabling us to easily plug in any future model as a microservice.
MLOps was born at the intersection of DevOps, Data Engineering, and Machine Learning, and is similar to DevOps. The execution is different. For a smart and balanced approach, we wished to anticipate our learning path somewhat, so we visited data science and forums, made lists of operations and testing painpoints, and ordered those in an inductive scenario.
Our backward planning path could look something like this:
Now we can aim for an architecture which enables us to easily plug in any future model as a microservice.
Oh well. Last orders, please. Waiter