Why We Need DevOps for ML Data

The industry needs to solve DevOps for ML data, as most ML projects fail to deploy and operate in production. The main challenge lies in accessing the right raw data source, building features from it, combining features into training data, calculating and serving features in production, monitoring features in production, and addressing related issues like data leakage, time travel, freshness, and ownership. Tecton, a centralized data platform for machine learning, aims to fill this vacuum by providing a feature pipeline, feature store, feature server, SDK, web UI, and monitoring engine to help ML teams bring DevOps practices to ML data, ensuring planability, code quality, build reliability, testing, release management, deployment, operation, and monitoring.