Glossary
MLOps
Definition
MLOps is the set of practices and tools for deploying, monitoring and maintaining machine learning models in production, including versioning of data, code and models.
Why it matters when you’re buying
Without it, models are hard to reproduce, audit or update, and quality drifts unnoticed. Look for lineage from any deployed model back to its data and approvals, and monitoring that covers quality as well as uptime.
How it relates to other terms
MLOps applies the habits of CI/CD to models: every change to data, code or a fine-tuned model is versioned, tested and deployed for inference through a pipeline.
Where to go next
- AI & ML Platforms: news, companies and community questions
- How to evaluate AI & ML Platforms vendors: criteria, questions to ask and red flags
- MLOps and inference: the latest stories and threads on this topic
Written by Techarda editors · Updated . Think a definition is wrong or missing something? Email engage@techarda.com. All terms.