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<!-- PROJECT LOGO -->
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<img alt="ZenML Logo" src="docs/book/.gitbook/assets/header.png" alt="ZenML Logo">
</a>
<br />
[![PyPi][pypi-shield]][pypi-url]
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[pypi-shield]: https://img.shields.io/pypi/pyversions/zenml?color=281158
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<h3 align="center">Create an MLOps workflow for your entire team.</h3>
<p align="center">
<div align="center">
Join our <a href="https://zenml.io/slack" target="_blank">
<img width="18" src="https://cdn3.iconfinder.com/data/icons/logos-and-brands-adobe/512/306_Slack-512.png" alt="Slack"/>
<b>Slack Community</b> </a> and be part of the ZenML family.
</div>
<br />
<a href="https://zenml.io/features">Features</a>
·
<a href="https://zenml.io/roadmap">Roadmap</a>
·
<a href="https://github.com/zenml-io/zenml/issues">Report Bug</a>
·
<a href="https://zenml.io/cloud">Sign up for Cloud</a>
·
<a href="https://www.zenml.io/blog">Read Blog</a>
·
<a href="https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22">Contribute to Open Source</a>
·
<a href="https://github.com/zenml-io/zenml-projects">Projects Showcase</a>
<br />
<br />
🎉 Version 0.56.4 is out. Check out the release notes
<a href="https://github.com/zenml-io/zenml/releases">here</a>.
<br />
🖥️ Download our VS Code Extension <a href="https://marketplace.visualstudio.com/items?itemName=ZenML.zenml-vscode">here</a>.
<br />
</p>
</div>
---
<!-- TABLE OF CONTENTS -->
<details>
<summary>🏁 Table of Contents</summary>
<ol>
<li><a href="#🤖-introduction">Introduction</a></li>
<li><a href="#🤸-quickstart">Quickstart</a></li>
<li>
<a href="#🖼️-learning">Learning</a>
</li>
<li><a href="#🗺-roadmap">Roadmap</a></li>
<li><a href="#🙌-contributing-and-community">Contributing and Community</a></li>
<li><a href="#🆘-getting-help">Getting Help</a></li>
<li><a href="#📜-license">License</a></li>
</ol>
</details>
<br />
# 🤖 Introduction
🤹 ZenML is an extensible, open-source MLOps framework for creating portable,
production-ready machine learning pipelines. By decoupling infrastructure from
code, ZenML enables developers across your organization to collaborate more
effectively as they develop to production.
- 💼 ZenML gives data scientists the freedom to fully focus on modeling and
experimentation while writing code that is production-ready from the get-go.
- 👨💻 ZenML empowers ML engineers to take ownership of the entire ML lifecycle
end-to-end. Adopting ZenML means fewer handover points and more visibility on
what is happening in your organization.
- 🛫 ZenML enables MLOps infrastructure experts to define, deploy, and manage
sophisticated production environments that are easy to use for colleagues.
<div align="center">
<img width="60%" src="/docs/book/.gitbook/assets/zenml-hero.png" alt="ZenML Hero"/>
</div>
# 🛠️ Why ZenML?
ZenML offers a systematic approach to structuring your machine learning codebase for a seamless transition to production. It's an ideal solution for teams grappling with establishing an internal standard for coordinating ML operations. ZenML provides not just a tool, but a workflow strategy that guides you in integrating all your tools and infrastructure effectively.
Use ZenML if:
- You need to easily automate ML workflows on services like an Airflow cluster or AWS Sagemaker Pipelines.
- Your ML tasks require repeatability and reproducibility.
- Automating and standardizing ML workflows across your team is a challenge.
- Your team integrates multiple tools with no central platform.
- You'd like a single place to track data, code, configuration, and models along with your cloud artifact storage.
- Collaboration and hand-overs between multiple teams is difficult.
# ☄️ What makes ZenML different?
ZenML marries the capabilities of a classic pipeline tool like [Airflow](https://airflow.apache.org/) and a metadata tracking service like [MLflow](https://mlflow.org/). Furthermore, both these types of tools can seamlessly co-exist with ZenML, providing a comprehensive, end-to-end ML experience.
It excels at:
- Enabling creation of simple, pythonic [ML pipelines](https://docs.zenml.io/user-guide/starter-guide/create-an-ml-pipeline) that function locally and on any [orchestration backend](https://docs.zenml.io/user-guide/production-guide/cloud-orchestration).
- Automating versioning of [data](https://docs.zenml.io/user-guide/starter-guide/manage-artifacts) and [models](https://docs.zenml.io/user-guide/starter-guide/track-ml-models) on [remote artifact storage like S3](https://docs.zenml.io/user-guide/production-guide/remote-storage).
- Abstracting infrastructure and run configuration from code through a [simple YAML config](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines).
- Logging complex [metadata](https://docs.zenml.io/user-guide/advanced-guide/data-management/logging-metadata) for models and artifacts.
- Automatically containerizing and deploying your workflows to the cloud, connected to your [code repository](https://docs.zenml.io/user-guide/production-guide/connect-code-repository).
- Connecting your [secret store](https://docs.zenml.io/user-guide/advanced-guide/secret-management) to your ML workflows.
However, ZenML doesn't:
- Automatically create visuals and track experiments: It [integrates with experiment trackers](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers) that specialize in this task.
- Package and deploy models: ZenML catalogs models and metadata, streamlining model deployment. Refer to [ZenML model deployers](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers) for more information.
- Handle distributed computation: While ZenML pipelines scale vertically with ease, it [works with tools like Spark](https://docs.zenml.io/stacks-and-components/component-guide/step-operators/spark-kubernetes) for intricate distributed workflows.
# 🤸 Quickstart
[Install ZenML](https://docs.zenml.io/getting-started/installation) via
[PyPI](https://pypi.org/project/zenml/). Python 3.8 - 3.11 is required:
```bash
pip install "zenml[server]"
# you'll also need the `notebook` package installed to run Jupyter notebooks:
# OPTIONALLY: `pip install notebook`
```
Take a tour with the guided quickstart by running:
```bash
zenml go
```
# 🔋 Deploy ZenML
For full functionality ZenML should be deployed on the cloud to
enable collaborative features as the central MLOps interface for teams.
<div align="center">
<img width="60%" src="docs/book/.gitbook/assets/Scenario3.png" alt="ZenML Architecture Diagram."/>
</div>
Currently, there are two main options to deploy ZenML:
- **ZenML Cloud**: With [ZenML Cloud](cloud.zenml.io/?utm_source=readme&utm_medium=referral_link&utm_campaign=cloud_promotion&utm_content=signup_link),
you can utilize a control plane to create ZenML servers, also known as tenants.
These tenants are managed and maintained by ZenML's dedicated team, alleviating
the burden of server management from your end.
- **Self-hosted deployment**: Alternatively, you have the flexibility to [deploy
ZenML on your own self-hosted environment](https://docs.zenml.io/deploying-zenml/zenml-self-hosted).
This can be achieved through various methods, including using our CLI, Docker,
Helm, or HuggingFace Spaces.
# 🖼️ 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.
For inspiration, here are some other examples and use cases:
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/llm-agents): 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.
# 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.
<details>
<summary>🖥️ VS Code Extension in Action!</summary>
<div align="center">
<img width="60%" src="/docs/book/.gitbook/assets/zenml-extension-shortened.gif" alt="ZenML Extension">
</div>
</details>
# 🗺 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.
# 🙌 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.
# 🆘 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.
# Vulnerability affecting `zenml<0.46.7` (CVE-2024-25723)
We have identified a critical security vulnerability in ZenML versions prior to
0.46.7. This vulnerability potentially allows unauthorized users to take
ownership of ZenML accounts through the user activation feature. Please [read our
blog post](https://www.zenml.io/blog/critical-security-update-for-zenml-users)
for more information on how we've addressed this.
# 📜 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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Check out the release notes\n <a href=\"https://github.com/zenml-io/zenml/releases\">here</a>.\n <br />\n \ud83d\udda5\ufe0f Download our VS Code Extension <a href=\"https://marketplace.visualstudio.com/items?itemName=ZenML.zenml-vscode\">here</a>.\n <br />\n </p>\n</div>\n\n---\n\n<!-- TABLE OF CONTENTS -->\n<details>\n <summary>\ud83c\udfc1 Table of Contents</summary>\n <ol>\n <li><a href=\"#\ud83e\udd16-introduction\">Introduction</a></li>\n <li><a href=\"#\ud83e\udd38-quickstart\">Quickstart</a></li>\n <li>\n <a href=\"#\ud83d\uddbc\ufe0f-learning\">Learning</a>\n </li>\n <li><a href=\"#\ud83d\uddfa-roadmap\">Roadmap</a></li>\n <li><a href=\"#\ud83d\ude4c-contributing-and-community\">Contributing and Community</a></li>\n <li><a href=\"#\ud83c\udd98-getting-help\">Getting Help</a></li>\n <li><a href=\"#\ud83d\udcdc-license\">License</a></li>\n </ol>\n</details>\n\n<br />\n\n# \ud83e\udd16 Introduction\n\n\ud83e\udd39 ZenML is an extensible, open-source MLOps framework for creating portable,\nproduction-ready machine learning pipelines. By decoupling infrastructure from\ncode, ZenML enables developers across your organization to collaborate more\neffectively as they develop to production.\n\n- \ud83d\udcbc ZenML gives data scientists the freedom to fully focus on modeling and\nexperimentation while writing code that is production-ready from the get-go.\n\n- \ud83d\udc68\u200d\ud83d\udcbb ZenML empowers ML engineers to take ownership of the entire ML lifecycle\n end-to-end. Adopting ZenML means fewer handover points and more visibility on\n what is happening in your organization.\n\n- \ud83d\udeeb ZenML enables MLOps infrastructure experts to define, deploy, and manage\nsophisticated production environments that are easy to use for colleagues.\n\n<div align=\"center\">\n <img width=\"60%\" src=\"/docs/book/.gitbook/assets/zenml-hero.png\" alt=\"ZenML Hero\"/>\n</div>\n\n# \ud83d\udee0\ufe0f Why ZenML?\n\n\n\nZenML offers a systematic approach to structuring your machine learning codebase for a seamless transition to production. It's an ideal solution for teams grappling with establishing an internal standard for coordinating ML operations. ZenML provides not just a tool, but a workflow strategy that guides you in integrating all your tools and infrastructure effectively.\n\nUse ZenML if:\n\n- You need to easily automate ML workflows on services like an Airflow cluster or AWS Sagemaker Pipelines.\n- Your ML tasks require repeatability and reproducibility.\n- Automating and standardizing ML workflows across your team is a challenge.\n- Your team integrates multiple tools with no central platform.\n- You'd like a single place to track data, code, configuration, and models along with your cloud artifact storage.\n- Collaboration and hand-overs between multiple teams is difficult.\n\n# \u2604\ufe0f What makes ZenML different?\n\n\n\nZenML marries the capabilities of a classic pipeline tool like [Airflow](https://airflow.apache.org/) and a metadata tracking service like [MLflow](https://mlflow.org/). Furthermore, both these types of tools can seamlessly co-exist with ZenML, providing a comprehensive, end-to-end ML experience.\n\nIt excels at:\n\n- Enabling creation of simple, pythonic [ML pipelines](https://docs.zenml.io/user-guide/starter-guide/create-an-ml-pipeline) that function locally and on any [orchestration backend](https://docs.zenml.io/user-guide/production-guide/cloud-orchestration).\n- Automating versioning of [data](https://docs.zenml.io/user-guide/starter-guide/manage-artifacts) and [models](https://docs.zenml.io/user-guide/starter-guide/track-ml-models) on [remote artifact storage like S3](https://docs.zenml.io/user-guide/production-guide/remote-storage).\n- Abstracting infrastructure and run configuration from code through a [simple YAML config](https://docs.zenml.io/user-guide/advanced-guide/pipelining-features/configure-steps-pipelines).\n- Logging complex [metadata](https://docs.zenml.io/user-guide/advanced-guide/data-management/logging-metadata) for models and artifacts.\n- Automatically containerizing and deploying your workflows to the cloud, connected to your [code repository](https://docs.zenml.io/user-guide/production-guide/connect-code-repository).\n- Connecting your [secret store](https://docs.zenml.io/user-guide/advanced-guide/secret-management) to your ML workflows.\n\nHowever, ZenML doesn't:\n\n- Automatically create visuals and track experiments: It [integrates with experiment trackers](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers) that specialize in this task.\n- Package and deploy models: ZenML catalogs models and metadata, streamlining model deployment. Refer to [ZenML model deployers](https://docs.zenml.io/stacks-and-components/component-guide/model-deployers) for more information.\n- Handle distributed computation: While ZenML pipelines scale vertically with ease, it [works with tools like Spark](https://docs.zenml.io/stacks-and-components/component-guide/step-operators/spark-kubernetes) for intricate distributed workflows.\n\n# \ud83e\udd38 Quickstart\n\n[Install ZenML](https://docs.zenml.io/getting-started/installation) via\n[PyPI](https://pypi.org/project/zenml/). Python 3.8 - 3.11 is required:\n\n```bash\npip install \"zenml[server]\"\n# you'll also need the `notebook` package installed to run Jupyter notebooks:\n# OPTIONALLY: `pip install notebook`\n```\n\nTake a tour with the guided quickstart by running:\n\n```bash\nzenml go\n```\n\n# \ud83d\udd0b Deploy ZenML\n\nFor full functionality ZenML should be deployed on the cloud to\nenable collaborative features as the central MLOps interface for teams.\n\n<div align=\"center\">\n <img width=\"60%\" src=\"docs/book/.gitbook/assets/Scenario3.png\" alt=\"ZenML Architecture Diagram.\"/>\n</div>\n\nCurrently, there are two main options to deploy ZenML:\n\n- **ZenML Cloud**: With [ZenML Cloud](cloud.zenml.io/?utm_source=readme&utm_medium=referral_link&utm_campaign=cloud_promotion&utm_content=signup_link), \nyou can utilize a control plane to create ZenML servers, also known as tenants. \nThese tenants are managed and maintained by ZenML's dedicated team, alleviating \nthe burden of server management from your end. \n\n- **Self-hosted deployment**: Alternatively, you have the flexibility to [deploy \nZenML on your own self-hosted environment](https://docs.zenml.io/deploying-zenml/zenml-self-hosted). \nThis can be achieved through various methods, including using our CLI, Docker, \nHelm, or HuggingFace Spaces.\n\n# \ud83d\uddbc\ufe0f Learning\n\nThe 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.\n\nFor inspiration, here are some other examples and use cases:\n\n1. [E2E Batch Inference](examples/e2e/): Feature engineering, training, and inference pipelines for tabular machine learning.\n2. [Basic NLP with BERT](examples/e2e_nlp/): Feature engineering, training, and inference focused on NLP.\n3. [LLM RAG Pipeline with Langchain and OpenAI](https://github.com/zenml-io/zenml-projects/tree/main/llm-agents): Using Langchain to create a simple RAG pipeline.\n4. [Huggingface Model to Sagemaker Endpoint](https://github.com/zenml-io/zenml-projects/tree/main/huggingface-sagemaker): Automated MLOps on Amazon Sagemaker and HuggingFace.\n\n# Use ZenML with VS Code\n\nZenML has a [VS Code\nextension](https://marketplace.visualstudio.com/items?itemName=ZenML.zenml-vscode)\nthat allows you to inspect your stacks and pipeline runs directly from your\neditor. The extension also allows you to switch your stacks without needing to\ntype any CLI commands.\n\n<details>\n <summary>\ud83d\udda5\ufe0f VS Code Extension in Action!</summary>\n <div align=\"center\">\n <img width=\"60%\" src=\"/docs/book/.gitbook/assets/zenml-extension-shortened.gif\" alt=\"ZenML Extension\">\n</div>\n</details>\n\n# \ud83d\uddfa Roadmap\n\nZenML is being built in public. The [roadmap](https://zenml.io/roadmap) is a\nregularly updated source of truth for the ZenML community to understand where\nthe product is going in the short, medium, and long term.\n\nZenML is managed by a [core team](https://zenml.io/company) of\ndevelopers that are responsible for making key decisions and incorporating\nfeedback from the community. The team oversees feedback via various channels,\nand you can directly influence the roadmap as follows:\n\n- Vote on your most wanted feature on our [Discussion\n board](https://zenml.io/discussion).\n- Start a thread in our [Slack channel](https://zenml.io/slack).\n- [Create an issue](https://github.com/zenml-io/zenml/issues/new/choose) on our\n GitHub repo.\n\n# \ud83d\ude4c Contributing and Community\n\nWe would love to develop ZenML together with our community! The best way to get\nstarted is to select any issue from the [`good-first-issue`\nlabel](https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22)\nand open up a Pull Request! If you\nwould like to contribute, please review our [Contributing\nGuide](CONTRIBUTING.md) for all relevant details.\n\n# \ud83c\udd98 Getting Help\n\nThe first point of call should\nbe [our Slack group](https://zenml.io/slack-invite/).\nAsk your questions about bugs or specific use cases, and someone from\nthe [core team](https://zenml.io/company) will respond.\nOr, if you\nprefer, [open an issue](https://github.com/zenml-io/zenml/issues/new/choose) on\nour GitHub repo.\n\n# Vulnerability affecting `zenml<0.46.7` (CVE-2024-25723)\n\nWe have identified a critical security vulnerability in ZenML versions prior to\n0.46.7. This vulnerability potentially allows unauthorized users to take\nownership of ZenML accounts through the user activation feature. Please [read our\nblog post](https://www.zenml.io/blog/critical-security-update-for-zenml-users)\nfor more information on how we've addressed this.\n\n# \ud83d\udcdc License\n\nZenML is distributed under the terms of the Apache License Version 2.0.\nA complete version of the license is available in the [LICENSE](LICENSE) file in\nthis repository. Any contribution made to this project will be licensed under\nthe Apache License Version 2.0.\n",
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