A home for machine learning projects built with ZenML and various integrations.

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☀️ Introducing ZenML Projects

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.

Project	Domain	Key Features	Core Technologies
ZenML Support Agent	🤖 LLMOps	🔍 RAG, 📊 Vector DB, 💬 Conversational	langchain, llama_index, openai
ZenCoder	🤖 LLMOps	🧠 Fine-tuning, 📈 Transfer Learning	huggingface, pytorch, wandb
Complete Guide to LLMs	🤖 LLMOps	🔍 RAG, 🧠 Fine-tuning, 📊 Evaluation	openai, huggingface, anthropic
Gamesense	🤖 LLMOps	🧠 LoRA, ⚡ Efficient Training	pytorch, peft, phi-2
Nightwatch AI	🤖 LLMOps	📝 Summarization, 📊 Reporting	openai, supabase, slack
ResearchRadar	🤖 LLMOps	📝 Classification, 📊 Comparison	anthropic, huggingface, transformers
End-to-end Computer Vision	👁️ Vision	🎯 Object Detection, 🏷️ Labeling	pytorch, label_studio, yolov8
Magic Photobooth	👁️ Vision	🖼️ Image Gen, 🎬 Video Gen	stable-diffusion, huggingface
Sign Language Detection	👁️ Vision	🎯 Object Detection, ⚡ Real-time	mlflow, bentoml, vertex-ai
Huggingface to Sagemaker	🚀 MLOps	🔄 CI/CD, 📦 Deployment	mlflow, sagemaker, kubeflow
Databricks Production QA	🚀 MLOps	📊 Monitoring, 🔍 Quality Assurance	databricks, evidently, shap
Eurorate Predictor	📊 Data	⏱️ Time Series, 🔄 ETL	airflow, bigquery, xgboost

💻 System Requirements

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 >=3.9

🪃 Contributing

We welcome contributions from anyone to showcase your project built using ZenML. See our contributing guide to start.

🆘 Getting Help

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.

🔥 About ZenML

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.

If you like these projects and want to learn more:

• Give the ZenML Repo a GitHub Star ⭐ to show your love!
• Join our Slack Community and become part of the ZenML family!

📜 License

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.

📖 Learn More

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.
⚽️ 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.