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Retrieval-Augmented Generation
Project Overview
A comprehensive machine learning pipeline that automates the entire ML workflow from data ingestion to model deployment. Features include automated feature engineering, model selection, hyperparameter tuning, and A/B testing capabilities.
The Problem
Data science teams needed an automated pipeline for consistent model development and deployment.
The Solution
Created an end-to-end ML pipeline with automated workflows and monitoring.
Key Challenges
- Model versioning
- Data pipeline reliability
- Deployment automation
Results
- 50% faster model development
- 99% pipeline reliability
- Automated A/B testing
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