MLOps

One model can be managed by hand; ten cannot. We set up the MLOps practice — experiment tracking, model registry, deployment pipelines and monitoring — on MLflow, Databricks or your cloud's native tools.

• Experiment tracking and model registryEvery model versioned with the data and code that produced it.
• Deployment pipelinesBatch and real-time serving, promoted through environments.
• MonitoringPerformance, drift and data quality in production.
• Data and model versioningReproducible results, auditable changes.
• Collaboration and governanceApprovals, documentation and access control.