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---
Need help with documentation? Visit our [docs site](https://docs.zenml.io) for comprehensive guides and tutorials, or browse the [SDK reference](https://sdkdocs.zenml.io/) to find specific functions and classes.
## βοΈ Show Your Support
If you find ZenML helpful or interesting, please consider giving us a star on GitHub. Your support helps promote the project and lets others know that it's worth checking out.
Thank you for your support! π
[](https://github.com/zenml-io/zenml/stargazers)
## π€Έ Quickstart
[](https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/quickstart.ipynb)
[Install ZenML](https://docs.zenml.io/getting-started/installation) via [PyPI](https://pypi.org/project/zenml/). Python 3.9 - 3.12 is required:
```bash
pip install "zenml[server]" notebook
```
Take a tour with the guided quickstart by running:
```bash
zenml go
```
## πͺ From Prototype to Production: AI Made Simple
### Create AI pipelines with minimal code changes
ZenML is an open-source framework that handles MLOps and LLMOps for engineers scaling AI beyond prototypes. Automate evaluation loops, track performance, and deploy updates across 100s of pipelinesβall while your RAG apps run like clockwork.
```python
from zenml import pipeline, step
@step
def load_rag_documents() -> dict:
# Load and chunk documents for RAG pipeline
documents = extract_web_content(url="https://www.zenml.io/")
return {"chunks": chunk_documents(documents)}
@step
def generate_embeddings(data: dict) -> None:
# Generate embeddings for RAG pipeline
embeddings = embed_documents(data['chunks'])
return {"embeddings": embeddings}
@step
def index_generator(
embeddings: dict,
) -> str:
# Generate index for RAG pipeline
index = create_index(embeddings)
return index.id
@pipeline
def rag_pipeline() -> str:
documents = load_rag_documents()
embeddings = generate_embeddings(documents)
index = index_generator(embeddings)
return index
```
### Easily provision an MLOps stack or reuse your existing infrastructure
The framework is a gentle entry point for practitioners to build complex ML pipelines with little knowledge required of the underlying infrastructure complexity. ZenML pipelines can be run on AWS, GCP, Azure, Airflow, Kubeflow and even on Kubernetes without having to change any code or know underlying internals.
ZenML provides different features to aid people to get started quickly on a remote setting as well. If you want to deploy a remote stack from scratch on your selected cloud provider, you can use the 1-click deployment feature either through the dashboard:
Or, through our CLI command:
```bash
zenml stack deploy --provider aws
```
Alternatively, if the necessary pieces of infrastructure are already deployed, you can register a cloud stack seamlessly through the stack wizard:
```bash
zenml stack register 
Slack Community and be part of the ZenML family.