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https://github.com/zenml-io/zenml

ZenML ๐Ÿ™: The bridge between ML and Ops. https://zenml.io.
https://github.com/zenml-io/zenml

ai automl data-science deep-learning devops-tools hacktoberfest llm llmops machine-learning metadata-tracking ml mlops pipelines production-ready pytorch tensorflow workflow zenml

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ZenML ๐Ÿ™: The bridge between ML and Ops. https://zenml.io.

Awesome Lists containing this project

README 

        


  

  
Beyond The Demo: Production-Grade AI Systems


  
ZenML brings battle-tested MLOps practices to your AI applications, handling evaluation, monitoring, and deployment at scale








  

  


    

      ZenML Logo

    

  



  [![PyPi][pypi-shield]][pypi-url]

  [![PyPi][pypiversion-shield]][pypi-url]

  [![PyPi][downloads-shield]][downloads-url]

  [![Contributors][contributors-shield]][contributors-url]

  [![License][license-shield]][license-url]

  

  






[pypi-shield]: https://img.shields.io/pypi/pyversions/zenml?color=281158



[pypi-url]: https://pypi.org/project/zenml/



[pypiversion-shield]: https://img.shields.io/pypi/v/zenml?color=361776



[downloads-shield]: https://img.shields.io/pypi/dm/zenml?color=431D93



[downloads-url]: https://pypi.org/project/zenml/



[codecov-shield]: https://img.shields.io/codecov/c/gh/zenml-io/zenml?color=7A3EF4



[codecov-url]: https://codecov.io/gh/zenml-io/zenml



[contributors-shield]: https://img.shields.io/github/contributors/zenml-io/zenml?color=7A3EF4



[contributors-url]: https://github.com/zenml-io/zenml/graphs/contributors



[license-shield]: https://img.shields.io/github/license/zenml-io/zenml?color=9565F6



[license-url]: https://github.com/zenml-io/zenml/blob/main/LICENSE



[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555



[linkedin-url]: https://www.linkedin.com/company/zenml/



[twitter-shield]: https://img.shields.io/twitter/follow/zenml_io?style=for-the-badge



[twitter-url]: https://twitter.com/zenml_io



[slack-shield]: https://img.shields.io/badge/-Slack-black.svg?style=for-the-badge&logo=linkedin&colorB=555



[slack-url]: https://zenml.io/slack-invite



[build-shield]: https://img.shields.io/github/workflow/status/zenml-io/zenml/Build,%20Lint,%20Unit%20&%20Integration%20Test/develop?logo=github&style=for-the-badge



[build-url]: https://github.com/zenml-io/zenml/actions/workflows/ci.yml



---



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.



## โญ๏ธ 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! ๐ŸŒŸ



[![Star this project](https://img.shields.io/github/stars/zenml-io/zenml?style=social)](https://github.com/zenml-io/zenml/stargazers)



## ๐Ÿคธ Quickstart

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](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

```



## ๐Ÿช„ 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 pipelinesโ€”all 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

```

![Running a ZenML pipeline](/docs/book/.gitbook/assets/readme_simple_pipeline.gif)



### 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:



![Running a ZenML pipeline](/docs/book/.gitbook/assets/one-click-deployment.gif)



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  --provider aws

```



Read more about [ZenML stacks](https://docs.zenml.io/user-guide/production-guide/understand-stacks).



### Run workloads easily on your production infrastructure



Once you have your MLOps stack configured, you can easily run workloads on it:



```bash

zenml stack set 

python run.py

```



```python

from zenml.config import ResourceSettings, DockerSettings



@step(

  settings={

    "resources": ResourceSettings(memory="16GB", gpu_count="1", cpu_count="8"),

    "docker": DockerSettings(parent_image="pytorch/pytorch:1.12.1-cuda11.3-cudnn8-runtime")

  }

)

def training(...):

	...

```



![Workloads with ZenML](/docs/book/.gitbook/assets/readme_compute.gif)



### Track models, pipeline, and artifacts



Create a complete lineage of who, where, and what data and models are produced.



You'll be able to find out who produced which model, at what time, with which data, and on which version of the code. This guarantees full reproducibility and auditability.



```python

from zenml import Model



@step(model=Model(name="rag_llm", tags=["staging"]))

def deploy_rag(index_id: str) -> str:

    deployment_id = deploy_to_endpoint(index_id)

    return deployment_id

```



![Exploring ZenML Models](/docs/book/.gitbook/assets/readme_mcp.gif)



## ๐Ÿš€ Key LLMOps Capabilities



### Continual RAG Improvement

**Build production-ready retrieval systems**  





  RAG Pipeline




ZenML tracks document ingestion, embedding versions, and query patterns. Implement feedback loops and:

- Fix your RAG logic based on production logs

- Automatically re-ingest updated documents

- A/B test different embedding models

- Monitor retrieval quality metrics



### Reproducible Model Fine-Tuning

**Confidence in model updates**





  Finetuning Pipeline




Maintain full lineage of SLM/LLM training runs:

- Version training data and hyperparameters

- Track performance across iterations

- Automatically promote validated models

- Roll back to previous versions if needed



### Purpose built for machine learning with integrations to your favorite tools



While ZenML brings a lot of value out of the box, it also integrates into your existing tooling and infrastructure without you having to be locked in.



```python

from bentoml._internal.bento import bento



@step(on_failure=alert_slack, experiment_tracker="mlflow")

def train_and_deploy(training_df: pd.DataFrame) -> bento.Bento

	mlflow.autolog()

	...

	return bento

```



![Exploring ZenML Integrations](/docs/book/.gitbook/assets/readme_integrations.gif)



## ๐Ÿ”„ Your LLM Framework Isn't Enough for Production



While tools like LangChain and LlamaIndex help you **build** LLM workflows, ZenML helps you **productionize** them by adding:



โœ… **Artifact Tracking** - Every vector store index, fine-tuned model, and evaluation result versioned automatically  

โœ… **Pipeline History** - See exactly what code/data produced each version of your RAG system  

โœ… **Stage Promotion** - Move validated pipelines from staging โ†’ production with one click  



## ๐Ÿ–ผ๏ธ Learning



The best way to learn about ZenML is the [docs](https://docs.zenml.io/). We recommend beginning with the [Starter Guide](https://docs.zenml.io/user-guide/starter-guide) to get up and running quickly.



If you are a visual learner, this 11-minute video tutorial is also a great start:



[![Introductory Youtube Video](docs/book/.gitbook/assets/readme_youtube_thumbnail.png)](https://www.youtube.com/watch?v=wEVwIkDvUPs)



And finally, here are some other examples and use cases for inspiration:



1. [E2E Batch Inference](examples/e2e/): Feature engineering, training, and inference pipelines for tabular machine learning.

2. [Basic NLP with BERT](examples/e2e_nlp/): Feature engineering, training, and inference focused on NLP.

3. [LLM RAG Pipeline with Langchain and OpenAI](https://github.com/zenml-io/zenml-projects/tree/main/zenml-support-agent): Using Langchain to create a simple RAG pipeline.

4. [Huggingface Model to Sagemaker Endpoint](https://github.com/zenml-io/zenml-projects/tree/main/huggingface-sagemaker): Automated MLOps on Amazon Sagemaker and HuggingFace

5. [LLMops](https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide): Complete guide to do LLM with ZenML



## ๐Ÿ“š Learn from Books





  

    LLM Engineer's Handbook Cover

  ย ย ย ย 

  

    Machine Learning Engineering with Python Cover

  

  




ZenML is featured in these comprehensive guides to modern MLOps and LLM engineering. Learn how to build production-ready machine learning systems with real-world examples and best practices.



## ๐Ÿ”‹ Deploy ZenML



For full functionality ZenML should be deployed on the cloud to

enable collaborative features as the central MLOps interface for teams.



Read more about various deployment options [here](https://docs.zenml.io/getting-started/deploying-zenml).



Or, sign up for [ZenML Pro to get a fully managed server on a free trial](https://cloud.zenml.io/?utm_source=readme&utm_medium=referral_link&utm_campaign=cloud_promotion&utm_content=signup_link).



## Use ZenML with VS Code



ZenML has a [VS Code extension](https://marketplace.visualstudio.com/items?itemName=ZenML.zenml-vscode) that allows you to inspect your stacks and pipeline runs directly from your editor. The extension also allows you to switch your stacks without needing to type any CLI commands.



  ๐Ÿ–ฅ๏ธ VS Code Extension in Action!

  


  ZenML Extension




## ๐Ÿ—บ Roadmap



ZenML is being built in public. The [roadmap](https://zenml.io/roadmap) is a regularly updated source of truth for the ZenML community to understand where the product is going in the short, medium, and long term.



ZenML is managed by a [core team](https://zenml.io/company) of developers that are responsible for making key decisions and incorporating feedback from the community. The team oversees feedback via various channels,

and you can directly influence the roadmap as follows:



- Vote on your most wanted feature on our [Discussion

board](https://zenml.io/discussion).

- Start a thread in our [Slack channel](https://zenml.io/slack).

- [Create an issue](https://github.com/zenml-io/zenml/issues/new/choose) on our GitHub repo.



## ๐Ÿ™Œ Contributing and Community



We would love to develop ZenML together with our community! The best way to get

started is to select any issue from the `[good-first-issue`

label](https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22)

and open up a Pull Request! 



If you

would like to contribute, please review our [Contributing

Guide](CONTRIBUTING.md) for all relevant details.



## ๐Ÿ†˜ Getting Help



The first point of call should

be [our Slack group](https://zenml.io/slack-invite/).

Ask your questions about bugs or specific use cases, and someone from

the [core team](https://zenml.io/company) will respond.

Or, if you

prefer, [open an issue](https://github.com/zenml-io/zenml/issues/new/choose) on

our GitHub repo.



## ๐Ÿ“š LLM-focused Learning Resources



1. [LL Complete Guide - Full RAG Pipeline](https://github.com/zenml-io/zenml-projects/tree/main/llm-complete-guide) - Document ingestion, embedding management, and query serving

2. [LLM Fine-Tuning Pipeline](https://github.com/zenml-io/zenml-projects/tree/main/zencoder) - From data prep to deployed model

3. [LLM Agents Example](https://github.com/zenml-io/zenml-projects/tree/main/zenml-support-agent) - Track conversation quality and tool usage



## ๐Ÿค– AI-Friendly Documentation with llms.txt



ZenML implements the llms.txt standard to make our documentation more accessible to AI assistants and LLMs. Our implementation includes:



- Base documentation at [zenml.io/llms.txt](https://zenml.io/llms.txt) with core user guides

- Specialized files for different documentation aspects:

  - [Component guides](https://zenml.io/component-guide.txt) for integration details

  - [How-to guides](https://zenml.io/how-to-guides.txt) for practical implementations

  - [Complete documentation corpus](https://zenml.io/llms-full.txt) for comprehensive access



This structured approach helps AI tools better understand and utilize ZenML's documentation, enabling more accurate code suggestions and improved documentation search.



## ๐Ÿ“œ License



ZenML is distributed under the terms of the Apache License Version 2.0.

A complete version of the license is available in the [LICENSE](LICENSE) file in

this repository. Any contribution made to this project will be licensed under

the Apache License Version 2.0.







    




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    ๐ŸŽ‰ Version 0.81.0 is out. Check out the release notes

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    ๐Ÿ–ฅ๏ธ Download our VS Code Extension here.