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
Deep Research	🤖 LLMOps	📝 Research, 📊 Reporting, 🔍 Web Search	anthropic, mcp, agents, openai
QualityFlow	🤖 LLMOps	🧪 Test Generation, 📊 Coverage Analysis, ⚡ Automation	openai, anthropic, pytest, jinja2
End-to-end Computer Vision	👁 CV	🔎 Object Detection, 🏷️ Labeling	pytorch, label_studio, yolov8
Magic Photobooth	👁 CV	📷 Image Gen, 🎞️ Video Gen	stable-diffusion, huggingface
OmniReader	👁 CV	📑 OCR, 📊 Evaluation, ⚙️ Batch Processing	polars, litellm, openai, ollama
Sign Language Detection	👁 CV	🔎 Object Detection, ⚡ Real-time	mlflow, bentoml, vertex-ai
Oncoclear	🚀 MLOps	📦 Deployment, 🔄 CI/CD	docker, kubernetes, scikit-learn
Huggingface to Sagemaker	🚀 MLOps	🔄 CI/CD, 📦 Deployment	mlflow, sagemaker, kubeflow
Databricks Production QA	🚀 MLOps	📊 Monitoring, 🔍 Quality Assurance	databricks, evidently, shap
Vertex Registry and Deployer	🚀 MLOps	📦 Model Registry, 🚀 Deployment	vertex, gcp, zenml
Eurorate Predictor	📊 Data	⏱️ Time Series, 🧹 ETL	airflow, bigquery, xgboost
RetailForecast	📊 Data	⏱️ Time Series, 📈 Forecasting, 🔄 Multi-Model	prophet, zenml, pandas
FloraCast	📊 Data	⏱️ Timeseries Prediction, 📈 Forecasting, 🔄 Batch Inference	darts, pytorch, zenml, pandas
Bank Subscription Prediction	📊 Data	💼 Classification, ⚖️ Imbalanced Data, 🔍 Feature Selection	xgboost, plotly, zenml
Credit Scorer	📊 Data	💰 Credit Risk, 📊 Explainability, 🇪🇺 EU AI Act	scikit-learn, fairlearn, zenml

💻 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.

Code Quality

All code contributions must pass our automated code quality checks:

• Code Formatting: We use ruff for code formatting and linting
• Spelling: We check for typos and spelling errors
• Markdown Links: We verify that all links in documentation work properly

Our CI pipeline will automatically check your PR for these issues. Remember to run bash scripts/format.sh locally before submitting your PR to ensure it passes the formatting checks.

🆘 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	Understand ZenML's building blocks.
🚀 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	Explore more sample projects.
📬 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	Got a PR or feature request? Start here.