(and why you need MLops and how)
Sources of inspiration : 1 book, 1 video and 1 article
This book from Mark Travel and the Dataiku team is a good point to start (like very their risk matrix). The video you should see is from Kaz Sato : I hope you will enjoy his sense of humor but more than that, it is a very good coverage with illustrations of ML best practices. And the article you should read has no author and can be found in the middle of Google Cloud documentation. It illustrates the different stages of MLops. I am not doing advertising for Google, just mentioning that this work is valuable.
You will find in these 3 sources all the concepts or way of doing in the right column. Rome was not build in a day… It is same for MLops. The challenge is really now to define stages and how you can avoid as much as possible the left column.
More than reducing the cycle time and increase quality : scale this subject !
MLops will help to reduce cycle time and increasing the model quality. But more than that, it is the key success factor for scaling this subject. It is easy to maintain 10 ML models, it is a totally different story with hundreds or thousands. The difference will not be about the talent war for data scientists, it is going about how to scale and become a “scalist”. Tools, organisation and people (as a team) is the core essence of MLops.