Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.

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MLOps Pipelines: From Experiment to Production Models

MLOps bridges experimentation and production. Here’s how to run reproducible training and deployment pipelines.

Pipeline Stages #

Data – Ingest, validate, version (e.g. DVC, Delta Lake).
Train – Reproducible training (code + data + config); track experiments (MLflow, Weights & Biases).
Evaluate – Holdout metrics, A/B tests, fairness checks.
Deploy – Model registry, versioned serving (API or batch); canary or shadow mode.
Monitor – Data drift, performance metrics, and alerts.

Reproducibility #

Pin code (git SHA), data (hash or version), and environment (container image).
Config as code (YAML/JSON) for hyperparameters and feature flags.

Best Practices #

Version models in a registry; promote only after evaluation passes.
Automate retraining or retraining triggers when drift or performance degrades.
Secure training data and model artifacts; audit access.

Start with a simple pipeline (train → eval → deploy) and add monitoring and automation as usage grows.

MLOps Pipelines: From Experiment to Production Models

MLOps Pipelines: From Experiment to Production Models

Pipeline Stages #

Reproducibility #

Best Practices #

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MLOps Pipelines: From Experiment to Production Models

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Best Practices#

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Pipeline Stages #

Reproducibility #

Best Practices #