MLOps

Full definition

MLOps adds to DevOps the lifecycle concerns specific to AI: training data versioning, feature stores, model registries, A/B and shadow deployment, drift and bias monitoring, automated retraining, and reproducibility. Mature MLOps platforms include Vertex AI, SageMaker, Databricks, MLflow, Kubeflow, and Weights & Biases.

Why it matters

Most AI projects that fail in production fail because of MLOps gaps, not modeling gaps. Gartner's surveys have repeatedly found that only about half of AI projects make it from pilot into production: 53% in its 2019 survey, 54% in 2022 and 48% in 2024, when going from prototype to production took 8 months on average. Investing in MLOps capability is what separates organizations that ship AI from those that perpetually pilot.

Example

A retailer's pricing model is retrained nightly on the prior day's transactions, evaluated against a held-out test set, gradually rolled out to 5% then 25% then 100% of stores via a feature flag, and monitored for drift — all automated through an MLOps pipeline.

Next steps

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Related terms

• AI Pipeline
  An AI pipeline is the end-to-end sequence of stages that turns raw data into deployed AI predictions or content — ingestion, cleaning, feature engineering, training, evaluation, deployment, monitoring, and retraining — usually orchestrated as code.
• Model Drift
  Model drift is the degradation of an AI model's performance over time as the data it sees in production diverges from the distribution it was trained on — the silent failure mode of every deployed ML system.
• AI Governance
  AI governance is the framework of policies, roles, controls, and processes an organization uses to ensure its AI systems are lawful, safe, fair, accountable, and aligned with business intent — across the full lifecycle from problem framing to retirement.
• Inference
  Inference is the process of running a trained AI model on new, unseen inputs to produce predictions, classifications, or generated content — the part that runs every time a user interacts with the system.
• Model
  An AI model is the trained artifact — a specific set of numerical weights plus an architecture — produced when a learning algorithm runs against training data, and the thing that actually gets deployed and audited.

More deployment & operations terms

• AI Agent / Agentic AI
  An AI agent is an LLM-driven system that, given a goal, plans a sequence of steps, calls tools or APIs, observes the results, and iterates until the goal is reached — going beyond single-turn chat to multi-step autonomous action.
• Tool Use (LLM)
  LLM tool use is the pattern in which a language model acts through external functions, APIs, databases or code execution: the model requests a tool, the host application runs it and returns the result, and the model continues, turning a text generator into a controller that can act. Function calling is the structured-output mechanism most tool use is built on.