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MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. It bridges the gap between machine learning development and production operations, ensuring that models are robust, scalable, and aligned with business goals.
Products, Architecture & Definition
Explore the main themes, entities and connections around MLOps. Start with the topic map, then use the sections below for research and deeper semantic analysis.
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learning machine systems models ml production devops business data development operations deployment continuous software lifecycle goals monitoring practice automation model
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| MLOps | is a | subset of ModelOps | 0.90 | text |
| Adaptive ML deliver commercial reinforcement learning operations | instance of | which can help enterprises optimize workflows and avoid issues during implementation.Vendors | 0.80 | text |
| MLOps | related to Definition | Most | 0.60 | section |
| MLOps | related to Definition | DevOps | 0.60 | section |
| MLOps | related to Definition | Dev | 0.60 | section |
| MLOps | related to Definition | Ops | 0.60 | section |
| MLOps | related to Definition | Essentially | 0.60 | section |
| MLOps | related to Definition | CI/CD | 0.60 | section |
| MLOps | related to Definition | ML | 0.60 | section |
| MLOps | related to Further reading | Huyen | 0.60 | section |
| MLOps | related to Further reading | Chip | 0.60 | section |
| MLOps | related to Further reading | Designing Machine Learning Systems | 0.60 | section |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.