prsdm/mlops-project
📦 Open Source Projectprsdm
A comprehensive end-to-end MLOps pipeline template for deploying scalable machine learning models using modern industry tools.
This project serves as a robust template for implementing a full-cycle MLOps lifecycle. It addresses the common challenges of reproducibility and scalability by incorporating industry-standard practices. Key technical features include data version control using DVC, which ensures data lineage and consistency across experiments. The project utilizes MLflow to manage the model lifecycle, including tracking parameters, code versions, and artifacts. For production readiness, the repository includes FastAPI for serving model predictions, Docker for containerization, and configuration for AWS ECS to facilitate cloud-native deployments. Furthermore, it integrates EvidentlyAI to monitor model performance and data drift, ensuring that deployed models maintain accuracy over time. By combining these tools, the project provides a modular and extensible framework that can be adapted for various machine learning use cases, making it an excellent resource for teams aiming to standardize their ML workflows.
💡Highlights
- ├─End-to-end ML lifecycle automation
- ├─Integrates DVC, MLflow, and EvidentlyAI
- └─Production-ready AWS ECS deployment
🎯For
- ├─ML Engineers
- ├─Data Scientists
- └─DevOps Engineers