# MLOps-Dagshub A streamlined MLOps pipeline integrating version control, model tracking, and reproducibility using DagsHub. Ideal for collaborative machine learning workflows and efficient experiment tracking. ## 🚀 Features - 📦 Data and model versioning with DVC and Git- 🧪 Experiment tracking and reproducibility- 📊 Visual insights into model performance- 🤝 Seamless collaboration via DagsHub’s web UI- 🛠️ CI/CD integration for model deployment (optional extension) ## 🔧 Tech Stack - Python The core programming language for building and orchestrating all components of your ML workflow—data processing, training scripts, API endpoints, and glue code for tools like DVC and MLflow. - DVC (Data Version Control) A Git-like system for versioning datasets, models, and pipeline stages. It helps you reproduce experiments and collaborate without storing large files in the Git repo, and enables pipeline orchestration with dvc.yaml. - Git Version control for code and lightweight files. Works in tandem with DVC to manage the code+data+model ecosystem in sync, allowing branching, reverting, and change tracking. - DagsHub A GitHub-integrated platform that brings Git, DVC, and experiment tracking together. Provides a UI to visualize experiments, compare runs, and store datasets and models—think of it as a collaborative MLOps dashboard. - MLflow An experiment tracking tool to log metrics, parameters, artifacts, and models. Lets you compare runs, register models, and optionally serve them via its model registry. - FastAPI A lightweight, high-performance Python web framework. Use it to serve trained models through RESTful API endpoints for real-time or batch predictions. ## 🏃 Getting Started Clone the repo and enter the directory: bash git clone https://github.com/your_username/MLOps-Dagshub.git cd MLOps-Dagshub

🔗 Quick Access

• 🔍 View Files
• 📂 Explore Datasets
• 🧪 Experiment Runs
• 📦 Registered Models

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