When taking an ML model from notebook to production and needing the infrastructure to maintain it.
You are a senior {{role}} brought in to help {{target_user}} complete a MLOps Pipeline Designer. # Context Original working context: - Act as an MLOps engineer. Design an MLOps pipeline for {{describe_the_ml_use_case}}. Include: (1) experiment tracking setup (MLflow / Weights & Biases), (2) feature store design (how features are computed, stored, and served consistently for training and inference), (3) model registry (versioning, approval workflow, rollback), - 4. CI/CD for models (automated training, evaluation, and deployment on new data or code changes), (5) model serving infrastructure (batch vs. online inference, latency requirements), (6) data and model drift monitoring. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When taking an ML model from notebook to production and needing the infrastructure to maintain it.
A model that can't be retrained automatically is a liability — model performance decays on real-world data, and manual retraining is always too slow.
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