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A repository for all ZenML projects that are specific production use-cases.

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

Get everything you need to start a project...
Features · Roadmap · Report Bug · Vote New Features · Read Blog · Meet the Team

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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

💻 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:

🔥 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:

📜 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.

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