In this article I would like to share useful links, which helped me to gain deeper understanding —

According to Wikipedia:

MLOps or ML Ops is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.

We are still here, and all-in DevOps :). Moving on …

Image is courtesy of AWS:

As any virtual asset ML has a lifecycle, and someone needs to take care of the pipeline, and here it comes which manages data, model training/retraining and model code.

To make it fair, I provide links to 2 cloud providers: and , other CSPs have similar whitepapers — it is on you to find it :)

has an excellent white paper defining, explaining application of principles in areas of Machine Learning.

Beside explaining technical components of ML, it also provides several implementation examples.

Image is courtesy of AWS:

I really do not want to copy/paste content here, it is definitely worth reading. Yet another excellent source is which explains key components of , and provides and.

has several good articles, I found and (not IBM) really helpful. The latter provides sample Implementation tutorial, with a good explanation of .

Image is courtesy of

Nevertheless, in all MLOps definitions —is an integral part of the pipeline (hah, similar to DevOps :))

So far, so good. But …

Do you ask yourself a question?

In DevOps pipeline is managing:

  1. New features
  2. Bug fixes
  3. Application architecture changes — for example, from monolith to microservices.
  4. Application runtime possible change, and so on…

What, then, is unique to MLOps ?

  1. Biased data, algorithms and concept drift — as bug fix
  2. Dataset may change (e.g external conditions change observations)
  3. CT- Continuous training and more…

There are many articles trying to explain MLOps vs DevOps, some links are below, in no particular order:


The main drivers in MLOps are and . Once you start understanding what is going on with data and models it becomes much easier to support MLOps pipeline.

There is an which explains what may happen with the model, and why monitoring is a must have.

And by the way, tools like and will make managing the MLOps pipeline much easier — but that is another story…

Hope you find this quick reference useful…



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