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Merge pull request #203 from zenml-io/add-omnireader-to-readme-table
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README.md

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@@ -51,25 +51,26 @@ integrations.</h3>
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This repository showcases production-grade ML use cases built with ZenML.
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The goal of this repository is to provide you a ready-to-use MLOps workflow that
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you can adapt for your application. We maintain a growing list of projects
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from various ML domains including time-series, tabular data, computer vision,
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you can adapt for your application. We maintain a growing list of projects
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from various ML domains including time-series, tabular data, computer vision,
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etc.
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| Project | Domain | Key Features | Core Technologies |
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|---------|--------|-------------|-------------------|
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| [ZenML Support Agent](zenml-support-agent) | 🤖 LLMOps | 🔍 RAG, 📊 Vector DB, 💬 Conversational | langchain, llama_index, openai |
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| [ZenCoder](zencoder) | 🤖 LLMOps | 🧠 Fine-tuning, 📈 Transfer Learning | huggingface, pytorch, wandb |
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| [Complete Guide to LLMs](llm-complete-guide) | 🤖 LLMOps | 🔍 RAG, 🧠 Fine-tuning, 📊 Evaluation | openai, huggingface, anthropic |
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| [Gamesense](gamesense) | 🤖 LLMOps | 🧠 LoRA, ⚡ Efficient Training | pytorch, peft, phi-2 |
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| [Nightwatch AI](nightwatch-ai) | 🤖 LLMOps | 📝 Summarization, 📊 Reporting | openai, supabase, slack |
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| [ResearchRadar](research-radar) | 🤖 LLMOps | 📝 Classification, 📊 Comparison | anthropic, huggingface, transformers |
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| [End-to-end Computer Vision](end-to-end-computer-vision) | 👁️ Vision | 🎯 Object Detection, 🏷️ Labeling | pytorch, label_studio, yolov8 |
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| [Magic Photobooth](magic-photobooth) | 👁️ Vision | 🖼️ Image Gen, 🎬 Video Gen | stable-diffusion, huggingface |
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| [Oncoclear](oncoclear) | 🚀 MLOps | 📦 Deployment, 🔄 CI/CD | docker, kubernetes, scikit-learn |
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| [Sign Language Detection](sign-language-detection-yolov5) | 👁️ Vision | 🎯 Object Detection, ⚡ Real-time | mlflow, bentoml, vertex-ai |
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| [Huggingface to Sagemaker](huggingface-sagemaker) | 🚀 MLOps | 🔄 CI/CD, 📦 Deployment | mlflow, sagemaker, kubeflow |
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| [Databricks Production QA](databricks-production-qa-demo) | 🚀 MLOps | 📊 Monitoring, 🔍 Quality Assurance | databricks, evidently, shap |
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| [Eurorate Predictor](eurorate-predictor) | 📊 Data | ⏱️ Time Series, 🔄 ETL | airflow, bigquery, xgboost |
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| Project | Domain | Key Features | Core Technologies |
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| --------------------------------------------------------- | --------- | ------------------------------------------ | ------------------------------------ |
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| [ZenML Support Agent](zenml-support-agent) | 🤖 LLMOps | 🔍 RAG, 📊 Vector DB, 💬 Conversational | langchain, llama_index, openai |
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| [ZenCoder](zencoder) | 🤖 LLMOps | 🧠 Fine-tuning, 📈 Transfer Learning | huggingface, pytorch, wandb |
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| [Complete Guide to LLMs](llm-complete-guide) | 🤖 LLMOps | 🔍 RAG, 🧠 Fine-tuning, 📊 Evaluation | openai, huggingface, anthropic |
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| [Gamesense](gamesense) | 🤖 LLMOps | 🧠 LoRA, ⚡ Efficient Training | pytorch, peft, phi-2 |
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| [Nightwatch AI](nightwatch-ai) | 🤖 LLMOps | 📝 Summarization, 📊 Reporting | openai, supabase, slack |
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| [ResearchRadar](research-radar) | 🤖 LLMOps | 📝 Classification, 📊 Comparison | anthropic, huggingface, transformers |
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| [End-to-end Computer Vision](end-to-end-computer-vision) | 👁️ Vision | 🎯 Object Detection, 🏷️ Labeling | pytorch, label_studio, yolov8 |
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| [Magic Photobooth](magic-photobooth) | 👁️ Vision | 🖼️ Image Gen, 🎬 Video Gen | stable-diffusion, huggingface |
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| [OmniReader](omni-reader) | 👁️ Vision | 📝 OCR, 📊 Evaluation, 🔄 Batch Processing | polars, litellm, openai, ollama |
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| [Oncoclear](oncoclear) | 🚀 MLOps | 📦 Deployment, 🔄 CI/CD | docker, kubernetes, scikit-learn |
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| [Sign Language Detection](sign-language-detection-yolov5) | 👁️ Vision | 🎯 Object Detection, ⚡ Real-time | mlflow, bentoml, vertex-ai |
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| [Huggingface to Sagemaker](huggingface-sagemaker) | 🚀 MLOps | 🔄 CI/CD, 📦 Deployment | mlflow, sagemaker, kubeflow |
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| [Databricks Production QA](databricks-production-qa-demo) | 🚀 MLOps | 📊 Monitoring, 🔍 Quality Assurance | databricks, evidently, shap |
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| [Eurorate Predictor](eurorate-predictor) | 📊 Data | ⏱️ Time Series, 🔄 ETL | airflow, bigquery, xgboost |
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# 💻 System Requirements
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