MLOps is the engineering practice of automating machine-learning workflows so teams can monitor data, track errors, evaluate models, and deliver updates faster. section 5.4.5 section 12.1.2
A practical MLOps workflow connects:
- Data monitoring and quality management, so new or changing data can be identified and used responsibly. section 5.4.5 section 12.1.2
- Error tracking and evaluation, using standardized benchmarks and application-specific metrics to guide improvements. section 5.4.5
- Model updates and deployment, integrating new data and insights into repeatable delivery workflows. section 5.4.5 section 12.1.2
Choosing infrastructure for an open-source language model
An organization deploying an open-source language model must balance model size, response latency, deployment cost, infrastructure demands, and output quality. MLOps infrastructure supports that ongoing trade-off rather than treating deployment as a one-time decision. section 5.3.2
