🏃 Get up and running quickly

Build your first ML pipelines with ZenML.

❓ Coming from zenml go ?

Then open notebooks/quickstart.ipynb to get started.

🌎 Overview

This quickstart demonstrates some of ZenML's features. We will:

• Import some data from a public dataset (Adult Census Income), then train two models (SGD and Random Forest)
• Compare and evaluate which model performs better, and deploy the best one.
• Run a prediction on the deployed model.

Along the way we will also show you how to:

• Automatically version, track, and cache data, models, and other artifacts,
• Track model hyperparameters and metrics in an experiment tracking tool

☁️ Run on Colab

You can use Google Colab to see ZenML in action, no signup / installation required!

💻 Run Locally

To run locally, install ZenML and pull this quickstart:

# Install ZenML
pip install "zenml[server]"

# clone the ZenML repository
git clone https://github.com/zenml-io/zenml.git
cd zenml/examples/quickstart

▶️ Run Locally

Now we're ready to start. You have two options for running the quickstart locally:

Option 1 (Recommended) - Interactively explore the quickstart using Jupyter Notebook:

pip install notebook
jupyter notebook
# open notebooks/quickstart.ipynb

Option 2 - Execute the whole ML pipeline from a Python script:

# Install required zenml integrations
zenml integration install sklearn mlflow -y

# Initialize ZenML
zenml init

# Start the ZenServer to enable dashboard access
zenml up

# Register required ZenML stack
zenml experiment-tracker register mlflow_tracker --flavor=mlflow
zenml model-deployer register mlflow_deployer --flavor=mlflow
zenml model-registry register mlflow_registry --flavor=mlflow

# Register a new stack with the new stack components
zenml stack register quickstart_stack -a default\
                                      -o default\
                                      -d mlflow_deployer\
                                      -e mlflow_tracker\
                                      -r mlflow_registry\
                                      --set

# Run the quickstart script
python run.py

🎯 Dashboard

You can also take a look at our dashboard where you can inspect the quickstart pipeline run and much more. Simply execute:

# only once you've already run `zenml up`
zenml show

🧽 Clean up

To clean up, simply spin down the ZenML server.

zenml down

💡 Learn More

If you want to learn more about ZenML as a tool, then the 📄 ZenML Docs are the perfect place to get started.

Already have an MLOps stack in mind? ZenML most likely has 🔗 Integrations for whatever tools you plan to use.

Also, make sure to join our Slack Community to become part of the ZenML family!