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 List

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, embeddings, finetuning	openai supabase huggingface argilla
LLM LoRA Finetuning (Phi3 and Llama 3.1)	NLP, LLMs	gcp
ECP Price Prediction with GCP Cloud Composer	Regression, Airflow	cloud-composer airflow
Simple LLM finetuning with Lightning Studio	Lightning AI Studio, LLMs	cloud-composer airflow
Flux Dreambooth	Flux, Dreambooth, LLMs	modal kubernetes

💻 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 3.7, 3.8, 3.9 or 3.10

🪃 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.
👨‍🍳 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.