MLOps applies operational discipline to machine learning, so models keep working after they ship. This guide explains what MLOps is, the lifecycle it covers, and how it extends to LLMs and agents.
MLOps (Machine Learning Operations) is the practice of building, deploying and operating machine learning models reliably in production: managing the lifecycle from experiment tracking and training through deployment, monitoring, and retraining, so models stay accurate and trustworthy over time.
MLOps spans the full model lifecycle: tracking experiments, managing data and features, training and versioning models, deploying them to serving infrastructure, and monitoring them in production. Governance, who can change what, and reproducibility run throughout. The goal is to treat models as operated products, not one-off artifacts.
A model that was accurate at launch degrades as the world changes, a phenomenon called drift. Data drift, concept drift and quality regressions mean a model needs continuous monitoring and, when it degrades, retraining or rollback. Without production operations, models silently get worse and no one notices until a decision goes wrong.
As applications shifted to large language models and AI agents, MLOps extended into LLMOps and Agent Ops: prompt versioning, retrieval pipeline observability, token-level cost tracking, and evaluation of open-ended outputs. Operating agents in production, watching their executions, cost, quality and failures, is the newest frontier.
Ops Singularity's AI/ML and Agent Ops pillar focuses on the operations side: live telemetry for models, agents, prompts and executions, drift and quality checks, cost and latency, and governed action when something degrades in production, complementing the platforms used to build and train models. Explore the AI/ML and Agent Ops pillar.
MLOps stands for Machine Learning Operations, the practice of building, deploying and operating machine learning models reliably in production across their full lifecycle.
LLMOps extends MLOps to large language model applications, adding capabilities classical ML did not need: prompt versioning, retrieval pipeline observability, token-level cost tracking and evaluation of open-ended outputs.
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