Machine Learning Ops: Operationalizing AI
Operationalizing AI with MLOps Machine learning ideas often start in notebooks, dashboards, or experiments. MLOps, short for machine learning operations, brings the methods from software teams into AI work. It helps data scientists turn ideas into reliable products, with clear ownership, repeatable processes, and safe updates when data changes. What MLOps covers Clear ownership and measurable goals for each model Repeatable data and model pipelines Versioning for data, code, and configurations Automated tests for data quality and model behavior Monitoring for drift, latency, and reliability Governance and audit trails for accountability A practical pipeline may look simple in steps: collect clean data, preprocess and split it, train and evaluate, register the model, deploy to staging, then push to production. In production, monitor inputs and outputs, track performance, and trigger retraining when needed. For example, a fraud detector could run daily checks and retrain if accuracy or precision drops. ...