MLflow Deployment
MLflow is the de facto standard for experiment tracking and model registry. But running it in production means more than 'pip install mlflow' — you need a proper tracking server, artifact backend, database, auth, and backups. We deploy MLflow as real infrastructure.
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Tracking Server Architecture
We deploy the MLflow tracking server as a containerized service behind a reverse proxy with TLS. The metadata backend uses PostgreSQL for durability and query performance. Artifact storage points to S3, GCS, or MinIO — not the local filesystem. The server handles concurrent experiment logging from multiple training jobs without bottlenecking.
Model Registry Configuration
The model registry gets configured with stage transitions: None to Staging to Production to Archived. Webhooks trigger on stage transitions — promoting a model to Production can automatically kick off a deployment pipeline. Model versions link back to the exact experiment run, parameters, and training data used.
Authentication & Access Control
MLflow's built-in auth (added in 2.5) or an OAuth2 proxy gates access to the UI and API. Service accounts with scoped permissions handle programmatic access from training pipelines. Network policies restrict tracking server access to known CIDR ranges. Sensitive experiment data stays internal.
Backup & High Availability
PostgreSQL gets automated daily backups with point-in-time recovery. Artifact storage inherits the durability of your object store (11 nines on S3). For teams that can't tolerate tracking server downtime, we deploy MLflow behind a load balancer with multiple replicas and session affinity. You get a production-grade ML platform, not a toy.
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