MLOps Pipelines: From Experiment to Production Models
Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.
Key takeaways
- Build MLOps pipelines for training, evaluation, and deployment.
- Reproducibility and monitoring.
On this page
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.
Get the DevOps Troubleshooting Cheat Sheet
Subscribe and get our free one-page reference for the errors that eat an afternoon — CrashLoopBackOff, OOMKilled, Terraform state locks, and more — plus new guides as we publish them.
Service Mesh Implementation: Istio vs Linkerd
We ran Istio for a year, then switched to Linkerd. Both can do the job. The decision came down to operational fit, not features.
Kubernetes Backup Strategies: Protecting Your Cluster Data
We've had to restore a Kubernetes cluster from backup twice. Once it worked. Once it took 14 hours. Here's the strategy we run now.
More from AI
Explore more articles in this category
AI Pair Programming: Tips to Get Better Code
Practical habits that turn AI coding assistants from a slot machine into a reliable pair, from context and prompts to verification.
Best AI Coding Assistants in 2026 — Compared by Use Case
Every AI coding tool demos beautifully. The real differences show up in your editor, your codebase, and your bill. This is the map to what each is best at.
Spec-Driven Development for AI Coding (2026)
Spec-driven development gives AI coding assistants an unambiguous target, so the output is reviewable, maintainable, and scales past throwaway scripts.
You might have missed
Evergreen posts worth revisiting.
