MLOps
Feature stores, model registries, monitoring, and CI/CD for ML systems
Foundations
Data Infrastructure
- MLOps
Feature Store
Training and serving often compute the same feature twice and drift apart. Define each feature once and let one store serve both paths.
- MLOps
Data Versioning
Code pins do not reproduce runs. Hash the rows, store the bytes once, and keep the small pointer beside the commit that used them.
- MLOps
ML Metadata
Every model is built from a dataset, a code commit, and a set of parameters. ML metadata writes that recipe down for each run, so you can trace any model back to exactly what made it.
Model Management
- MLOps
Model Registry Platform: Stages to Rollback
A registry platform versions every model artifact with stage labels and lineage, so promoting v3 to Production archives v2 in the same move and rollback means repointing one alias instead of hunting files.
- MLOps
CI/CD in ML Pipelines
ML automation tests data and retrains models alongside code. New rows, drift alerts, or a schedule can all trigger a fresh model, not just code commits.
- MLOps
Experiment Tracking
Tuning makes dozens of runs that all look alike. Log settings, code, data, and scores per run so the winner is a row you can rerun.
- MLOps
Pipeline Orchestration: DAGs That Resume
Orchestration turns scattered ML scripts into a dependency graph of containerized steps, so when the 38 minute train step fails only it reruns while the finished 4 minute prep result is reused.
- MLOps
Infrastructure as Code for ML
You declare your GPU clusters, buckets, and serving endpoints in version-controlled files, and a tool builds them identically every time, so staging matches production instead of merely resembling it.
- MLOps
Reproducibility in MLOps: Same Model Twice
Reproducibility pins code, data, environment, and randomness so thoroughly that rebuilding months later produces byte-identical weights, turning debugging and audits from archaeology into replay.
- MLOps
Cost Optimization in ML
Serving pays for GPUs even when traffic sleeps. Match replicas to demand so p95 stays flat while idle hours drop toward zero.
Serving
- MLOps
Model Serving: Real-Time API vs Batch
Serving turns a trained model into something other systems can call: a real-time API that answers one request in milliseconds, or a batch job that scores millions of rows overnight and parks the answers in a table.
- MLOps
Blue-Green Deployments
Two identical production environments take turns serving users. You build and check the new model in the quiet one, then flip the router so all traffic moves at once.
Observability
- MLOps
Model Monitoring Platform: Drift to Alert
A monitoring platform watches every live prediction window against training baselines and pages the owner when drift, output, or latency crosses a rule, so a silent decay becomes a ticket before users notice.
- MLOps
A/B Testing
Two models serve live traffic at the same time, and the one that moves the business number wins. You judge them on real user behavior, not offline scores.
- MLOps
Canary Deployments in ML
A new model serves a small slice of real traffic first. If it misbehaves, only that slice feels it, and the rest stay on the proven model.
- MLOps
Model Drift Detection
Models freeze the past while the world keeps moving. Drift detection compares live traffic to training data on a schedule, so a shifted input or a changed relationship raises an alert before users pay for it.
- MLOps
Data Quality Monitoring
Models fail quietly on bad rows. Check schema, null share, and value ranges on every batch so a broken pipe halts before it scores.
- MLOps
ML Testing Strategies
Green code tests do not mean a safe model. Test the pipeline code, the incoming data, and the trained behavior separately, because each one fails in its own way.