Infrastructure
MLOps
The practices and tools for reliably deploying, monitoring, and maintaining machine learning models in production.
Definition
The discipline of applying DevOps principles to machine learning. Covers model versioning, automated training pipelines, deployment, monitoring, and retraining. Tools include MLflow, Kubeflow, and Weights & Biases.
Why it matters
87% of ML models never make it to production. MLOps is the bridge between data science experiments and business value.
Related terms in Infrastructure
API
A digital plug or messenger that lets two different software programs talk to each other.
API Gateway
The security guard at the front door of your software that checks IDs and directs traffic.
Cloud Computing
Renting powerful computers over the internet instead of buying and keeping them in your own office.
Edge AI
Running AI directly on the device (phone, camera, car) instead of sending data to the cloud, faster and more private.
From vocabulary to outcomes
Ready to put MLOps to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
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