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	Resources	Tags	Stack Components
NBA Three-Pointer Predictor		Time-series	mlflow kubeflow evidently sklearn aws discord
Time Series Forecasting		Time-series	vertex
Customer Satisfaction		Tabular	mlflow kubeflow
Customer Churn		Tabular	kubeflow seldon
YOLOv5 Object Detection		Computer-vision	mlflow vertex
GitFlow ZenML Project		MLOps with ZenML and GitHub Workflows	mlflow deepchecks kserve kubeflow sklearn vertex aws gcp
ZenNews		NLP	gcp vertex discord
Langchain Slackbot		NLP, LLMs	slack langchain llama_index
Orbit User Analysis		Data Analysis, Tabular

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.

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!

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.