MLOps
Deploy and manage machine learning models in production with MLOps best practices.
How it works
Generates ML pipeline infrastructure: model training workflows, experiment tracking, model registry, A/B testing for model versions, and monitoring for data drift. Handles MLflow/W&B integration, feature stores, and automated retraining triggers.
Example prompts
“Set up MLflow for experiment tracking and model registry”
“Build an automated retraining pipeline triggered by data drift”
“Create A/B testing infrastructure for ML model versions”
Real-world example
A fintech company built their ML pipeline: automated feature engineering from transaction data, MLflow experiment tracking, model registry with approval gates, and canary deployments that roll back automatically if precision drops below threshold.
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