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MLOps components

1. MLOps components

In this video, we will talk about the fundamental components of an MLOps framework and their mutual interaction.

2. Concepts

We will cover general software development concepts such as workflows, pipelines, and artifacts; and ML-specific ones such as the model registry, feature store, and metadata store.

3. DevOps

MLOps is a extension of the DevOps software development principles

4. to MLOps

to include ML workflows.

5. Workflows

A workflow is a generic business term for any sequence of tasks that, starting from certain inputs, produces certain outputs.

6. Workflow execution

Workflows can be executed

7. Manual

manually,

8. Automatic

automatically,

9. Semi-automatic

or semi-automatically.

10. Workflow to Pipeline

To automate a workflow

11. Scripting

we need to write a program, often called "a script," for every action within it.

12. Pipeline definition

We call this program a “pipeline,” and it has become a catch-all term for any end-to-end automated workflow in IT, be it in Dev-, Data- or MLOps.

13. Artifacts

The outputs of pipelines are called artifacts, which is actually another catch-all term

14. Artifacts II

for any output of the software development process.

15. ML build pipelines

Let's talk about ML-specific pipelines now. We said that the model life cycle starts with the deployment

16. To have something to deploy

but to have something to deploy,

17. We must build it first

we must build it first. To build means to transform pure code and data into deployable applications and models. Think of something like a software installer that you would download from the Internet, but for ML applications. That is the purpose of so-called build pipelines. In MLOps we have at least two separate build workflows:

18. One for the model

one for building the model itself

19. One for the app

and one for building the ML application that will serve our model.

20. App build pipeline

The app build process is the classical DevOps software build. It takes our scripts from a central repository, where we manage all our code, makes a few automated transformations that package our app for deployment, and stores that package in a dedicated repository.

21. Model build pipeline

But the model build pipeline, also known as the model training pipeline, is a completely different thing. Apart from the code containing the model definition and the training script

22. We now also need the data

we now also need the data to train on. This single extra ingredient adds a whole set of complexities.

23. We can start from raw

Our training pipeline can start from raw

24. Or already processed

or already processed data.

25. Feature store

We call processed variables for model training "features" and a database that stores them a "feature store".

26. Model build artifacts

After the pipeline is

27. Successfully executed

successfully executed, we end up with the

28. Trained model artifact

trained model object and

29. collection of data and scripts

complementary data necessary for its deployment and life-cycling. We call this complementary data "model metadata".

30. Model registry

We now push the model object into our model registry, which is a specialized registry for storing and versioning ML models,

31. Metadata store

and the metadata into the so-called metadata store, whose purpose is to store all important information about the different models we build and deploy.

32. Deployment pipeline

Now we have something to deploy, but we need the how. That's the job of, you guessed it: deployment pipelines.

33. Take the build

Their job is to take the build artifacts -- in our case the ML app package and the model package --

34. Set them in place

and set them in place on the target serving platform. Once their job is done, we're locked, loaded, and ready to go.

35. Monitoring

We can now start our service and monitor if it runs and performs as expected.

36. The whole picture

And that is a typical high-level MLOps architecture. If this is a lot of new concepts all at once, don't worry: we will take enough time to explain each of them in the following chapters.

37. Let's practice!

Now, let's see how much you have managed to absorb.