ML Engineering January 10, 2024 1 min read
Building Scalable ML Pipelines: Best Practices
A comprehensive guide to designing and implementing production-ready machine learning infrastructure.
Why ML Pipelines Matter
Machine learning pipelines are the backbone of any production ML system. They ensure consistency, reproducibility, and scalability.
Core Components
- Data Ingestion: Reliable data collection from multiple sources
- Feature Engineering: Automated feature extraction and transformation
- Model Training: Scalable training infrastructure with experiment tracking
- Model Serving: Low-latency inference with A/B testing capabilities
Technology Stack
Our recommended stack includes:
- Apache Airflow for orchestration
- MLflow for experiment tracking
- Kubernetes for containerized deployments
- Feature stores like Feast for feature management