A home for machine learning projects built with ZenML and various integrations.
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This repository showcases production-grade ML use cases built with ZenML. The goal of this repository is to provide you a ready-to-use MLOps workflow that you can adapt for your application. We maintain a growing list of projects from various ML domains including time-series, tabular data, computer vision, etc.
A list of updated and maintained projects by the ZenML team and the community:
| Project | Tags | Integrations |
|---|---|---|
| NBA Three-Pointer Predictor | Time-series | mlflow kubeflow evidently sklearn aws |
| Time Series Forecasting | Time-series | gcp |
| Customer Satisfaction | Tabular | mlflow kubeflow |
| Customer Churn | Tabular | kubeflow seldon |
| Label Studio Annotation | Data Annotation | label-studio |
| YOLOv5 Object Detection | Computer-vision | mlflow gcp |
| LLMs To Analyze Databases | NLP, LLMs | gcp slack |
| GitFlow ZenML Project | MLOps with ZenML and GitHub Workflows | mlflow deepchecks kserve kubeflow sklearn vertex aws gcp |
| ZenNews | NLP | gcp vertex discord |
| LLM RAG Pipeline with Langchain and OpenAI | NLP, LLMs | slack langchain llama_index |
| Orbit User Analysis | Data Analysis, Tabular | - |
| Huggingface to Sagemaker | NLP | pytorch mlflow huggingface aws s3 kubeflow slack github |
| Complete Guide to LLMs (from RAG to finetuning) | NLP, LLMs | openai supabase |
To run any of the projects listed, you have to install ZenML on your machine. Read our docs for installation details.
- Linux or macOS.
- Python 3.7, 3.8, 3.9 or 3.10
We welcome contributions from anyone to showcase your project built using ZenML. See our contributing guide to start.
By far the easiest and fastest way to get help is to:
- Ask your questions in our Slack group.
- Open an issue on our GitHub repo.
- Meet the team every week during our community meetup.
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.
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ZenML Projects is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in the LICENSE file in this repository. Any contribution made to this project will be licensed under the Apache License Version 2.0.
| ZenML Resources | Description |
|---|---|
| 🧘♀️ ZenML 101 | New to ZenML? Here's everything you need to know! |
| ⚛️ Core Concepts | Some key terms and concepts we use. |
| 🚀 Our latest release | New features, bug fixes. |
| 🗳 Vote for Features | Pick what we work on next! |
| 📓 Docs | Full documentation for creating your own ZenML pipelines. |
| 📒 API Reference | Detailed reference on ZenML's API. |
| 👨🍳 MLStacks | Terraform-based infrastructure recipes for pre-made ZenML stacks. |
| ⚽️ Examples | Learn best through examples where ZenML is used. We've got you covered. |
| 📬 Blog | Use cases of ZenML and technical deep dives on how we built it. |
| 🔈 Podcast | Conversations with leaders in ML, released every 2 weeks. |
| 💬 Join Slack | Need help with your specific use case? Say hi on Slack! |
| 🗺 Roadmap | See where ZenML is working to build new features. |
| 🙋♀️ Contribute | How to contribute to the ZenML project and code base. |