Quality Control

Quality control in the context of OpsML refers to:

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.}