An end-to-end project
Put your new knowledge in action with an end to end project
In this guide, we will go over some advanced concepts:
The value of deploying ZenML
Abstracting infrastructure configuration into stacks
Deploying an MLOps stack on a cloud provider of your choice
Setting up pipeline configuration in production
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:
We will then use ZenML templates to help us get the code we need for the project:
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:
Designing ZenML pipeline steps
Using step parameterization and step caching to design flexible and reusable steps
Constructing and running a ZenML pipeline
Accessing ZenML pipeline run artifacts in the post-execution phase after a pipeline run has concluded
Best practices for implementing and running reproducible and reliable ML pipelines with ZenML
Now try sharing the ZenML e2e template with your colleagues and see how they react!
Conclusion and next steps
The production guide has now hopefully landed you with an end-to-end MLOps project, powered by a ZenML server connected to your cloud infrastructure. You are now ready to dive deep into writing your own pipelines and stacks. If you are looking to learn more advanced concepts, the Advanced Guide is for you. Until then, we wish you the best of luck chasing your MLOps dreams!
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