An end-to-end project

Put your new knowledge in action with an end-to-end project

That was awesome! We learned so many advanced MLOps production concepts:

The value of deploying ZenML

Abstracting infrastructure configuration into stacks

Connecting remote storage

Orchestrating on the cloud

Configuring the pipeline to scale compute

Connecting a git repository

We will now combine all of these concepts into an end-to-end MLOps project powered by ZenML.

Get started

Start with a fresh virtual environment with no dependencies. Then let's install our dependencies:

pip install "zenml[templates,server]" notebook

zenml integration install sklearn -y

We will then use ZenML templates to help us get the code we need for the project:

mkdir zenml_batch_e2e

cd zenml_batch_e2e

zenml init --template e2e_batch --template-with-defaults

# Just in case, we install the requirements again

pip install -r requirements.txt

The e2e template is also available as a ZenML example. You can clone it:

git clone --depth 1 git@github.com:zenml-io/zenml.git

cd zenml/examples/e2e

pip install -r requirements.txt

zenml init

What you'll learn

The e2e project is a comprehensive project template to cover major use cases of ZenML: a collection of steps and pipelines and, to top it all off, a simple but useful CLI. It showcases the core ZenML concepts for supervised ML with batch predictions. It builds on top of the starter project with more advanced concepts.

As you progress through the e2e batch template, try running the pipelines on a remote cloud stack on a tracked git repository to practice some of the concepts we have learned in this guide.

At the end, don't forget to share the ZenML e2e template with your colleagues and see how they react!

Conclusion and next steps