ML Data Pipeline System
Scalable Data Processing for Machine Learning at Scale
Overview
A production-grade data processing pipeline designed to handle massive-scale machine learning workloads, processing over 10TB of data daily with automated preprocessing and feature engineering.
The system implements best practices in MLOps, including automated data validation, feature stores, model versioning, and continuous training pipelines to ensure reliable and reproducible machine learning at scale.
Key Features
10TB+ Daily Processing
Handle massive data volumes with distributed computing architecture.
Automated Preprocessing
Intelligent data cleaning, normalization, and transformation pipelines.
Feature Engineering
Automated feature extraction and selection for ML models.
Continuous Training
Automated model retraining and deployment pipelines.
Technologies Used
System Architecture
1. Data Ingestion
Scalable data ingestion from multiple sources including databases, APIs, and streaming platforms with schema validation and data quality checks.
2. Distributed Processing
Apache Spark-based distributed computing for handling large-scale data transformations and feature computation.
3. Feature Store
Centralized feature repository enabling feature reuse, versioning, and serving for both training and inference.
4. ML Pipeline
Orchestrated training pipelines with experiment tracking, model versioning, and automated deployment to production.