Quality Control

Quality control in OpsML encompasses the essential processes and best practices that ensure the reliability, integrity, availability, and governance of machine learning artifacts. OpsML streamlines these critical tasks so data scientists can focus on modeling, and engineers get the robust controls they need for production environments.

Developer-First Experience

• Zero-friction Integration: Drop into existing ML workflows in minutes
• Type-safe and efficient by Design: Rust in the back, python in the front^*. Catch errors before they hit production
• Unified API: One consistent interface for all ML frameworks
• Environment Parity: Same experience from development to production
• Dependency Overhead: One dependency for all ML artifact management

Built to Scale

• Trading Cards for ML: Manage ML artifacts like trading cards - collect, organize, share
• Cloud-Ready: Native support for AWS, GCP, Azure
• Database Agnostic: Support for SQLite, MySQL, Postgres
• Modular Design: Use what you need, leave what you don't

Production Ready

• High-Performance Server: Built in Rust for speed, reliability and concurrency
• Built-in Security: Authentication and encryption out of the box
• Audit-Ready: Complete artifact lineage and versioning
• Standardized Governance Workflows: Consistent patterns to use across teams
• Built-in Monitoring: Integrated with Scouter

^{*OpsML is written in Rust and is exposed via a Python API built with PyO3.}