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	Tools
LLM Agents	NLP LLM Agents Conversational AI RAG Vector Stores Production MLOps	langchain llama_index faiss openai
LLM Finetuning	NLP LLM Model Fine-tuning Transfer Learning Parameter Optimization	huggingface pytorch wandb
Complete Guide to LLMs	NLP LLM RAG Fine-tuning Model Evaluation Embeddings Synthetic Data	openai supabase huggingface argilla gradio anthropic litellm
LLM LoRA Finetuning	NLP Parameter-Efficient Fine-tuning LoRA LLM Distributed Training	huggingface pytorch accelerate peft phi-2
End-to-end Computer Vision	Computer Vision Object Detection Data Labeling Human-in-the-Loop	pytorch label_studio fiftyone vertex-ai gcp yolov8
Flux Dreambooth	Image Generation Fine-tuning Stable Diffusion LoRA Video Generation	modal kubernetes huggingface flux stable-video-diffusion
Huggingface to Sagemaker	Model Deployment NLP Sentiment Analysis Model Training CI/CD	pytorch mlflow huggingface aws sagemaker s3 kubeflow slack github
Databricks Production QA Demo	Quality Assurance CI/CD Model Monitoring Model Explainability Data Drift	databricks mlflow evidently shap slack
ECB Interest Rate Prediction with GCP Cloud Composer	ETL Time Series Feature Engineering Regression Workflow Orchestration	cloud-composer airflow vertex-ai bigquery xgboost gcp
Supabase OpenAI Summary	NLP Text Summarization Database Integration LLM Automated Reporting	openai supabase slack github-actions gcp
Sign Language Detection with YOLOv5	Computer Vision Object Detection Real-time Processing Model Deployment	mlflow gcp bentoml vertex-ai docker

💻 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.