Project Overview

Automated Machine Learning Pipeline Platform

An end-to-end ML pipeline platform that automates the entire machine learning lifecycle from data ingestion to model deployment. The platform supports feature engineering, model training, hyperparameter tuning, and automated deployment.

Built with MLOps best practices, the platform includes version control for models, experiment tracking, model registry, and automated retraining pipelines. It supports various ML frameworks including TensorFlow, PyTorch, and scikit-learn.

Key Features

• Automated ML lifecycle management
• Feature engineering and data preprocessing
• Model training and hyperparameter tuning
• Model versioning and registry
• Automated model deployment and monitoring

The platform has enabled clients to deploy 200+ ML models into production, reducing model development time by 60% and improving model accuracy through automated optimization.