MLOps

MLOps (Machine Learning Operations) is the discipline of automating, monitoring, and reliably operating machine learning systems in production. Inspired by DevOps, MLOps adds practices specific to ML: experiment tracking, model registries, feature stores, automated retraining, drift detection, and shadow deployment. Tools include MLflow (open-source experiment tracking), Weights & Biases, Kubeflow, SageMaker (AWS), Vertex AI (Google), Azure ML, Databricks Mosaic AI, and platforms like Modal, Replicate, and Anyscale. MLOps emerged in the late 2010s as enterprises realized that getting models into production reliably was harder than building them. The discipline now underpins virtually every production machine learning system at scale. AI governance, AI compliance, and AI risk management programs lean heavily on MLOps tooling — model registries become the inventory, experiment logs become AI audit evidence, and drift detection becomes the operational backbone of responsible AI in enterprise environments at scale.

Centralpoint Is MLOps With Governance Built In: Oxcyon's Centralpoint AI Governance Platform combines MLOps discipline with model-agnostic AI access (OpenAI, Gemini, Llama, embedded). Centralpoint meters every LLM call, keeps prompts and skills on-prem, and embeds production-grade chatbots into your portals with one line of JavaScript.


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