THIS REPOSITORY IS GOING TO BE DEPRECATED AFTER 01/08/2023.

We are now offering ZenML Project Templates - a configurable way to rocketstart your ZenML journey! For more details about templates you can use zenml init --help.

ZenBytes

ZenBytes is a series of short practical MLOps lessons through ZenML and its various integrations. It is intended for people looking to learn about MLOps generally, and also for ML practitioners who want to get started with ZenML.

💡 What you will learn

Define an MLOps stack tailored to your project requirements.
Build transparent and reproducible data-centric ML pipelines with automated artifact versioning, tracking, caching, and more.
Deploy ML pipelines with tooling and infrastructure of your choice (e.g. as a serverless microservice in the cloud).
Monitor and address production issues like training-serving skew and data drift.
Use some of the most popular MLOps tools like ZenML, Kubeflow, MLflow, Weights & Biases, Evidently, Seldon, Feast, and many more.

In the end, you will be able to take any of your ML models from experimentation to a customized, fully fleshed-out production-grade MLOps setup in a matter of minutes!

🧑‍🏫 Syllabus

The series is structured into four chapters with several lessons each. Click on any of the links below to open the respective lesson directly in Colab.

🍡 1. ML Pipelines	♻️ 2. Training / Serving	📁 3. Data Management
1.1 ML Pipelines	2.1 Experiment Tracking	3.1 Data Skew
1.2 Artifact Lifecycle	2.2 Local Deployment
	2.3 Inference Pipelines

🙏 About ZenML

ZenML is an extensible, open-source MLOps framework for creating production-ready ML pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows.

If you enjoy these courses and want to learn more:

Give the Main ZenML Repo a GitHub Star ⭐ to show your love!
Join our Slack Community and become part of the ZenML family!

💻 Setup

System Requirements

Linux or MacOS
Python 3.7, 3.8, 3.9, or 3.10
Jupyter notebook and ZenML: pip install zenml notebook

Integrations

As you progress through the course, you will need to install additional packages for the various other MLOps tools we will use. You will find corresponding instructions in the respective notebooks, but we recommend you install all integrations ahead of time with the following command:

zenml integration install sklearn wandb evidently mlflow -y

🚀 Getting Started

If you haven't done so already, clone ZenBytes to your local machine. Then, use Jupyter Notebook to go through the course lesson-by-lesson, starting with 1-1_Pipelines.ipynb:

git clone https://github.com/zenml-io/zenbytes
cd zenbytes
jupyter notebook

❓ FAQ

1. ZenML cannot find a component even though I have it in my stack

Updating or switching your ZenML stack is sometimes not immediately loaded in Jupyter notebooks.

Solution: First, make sure you really have the correct component installed and registered in your currently active stack with zenml stack describe. If the component is indeed there, restart the kernel of your Jupyter notebook, which will also reload the stack.

anothermorena / zenbytes