Data Platform on Databricks – MLOps | CloudBoostUP

Automate the ML lifecycle on Databricks. Experiment tracking, model deployment, monitoring, all managed as code.

Who this is for

You have data pipelines in place and want to operationalise machine learning. Your data scientists are training models in notebooks but there is no repeatable path to production. You need MLOps discipline without building the platform from scratch.

What we deliver

• Experiment Tracking: MLflow-based tracking for parameters, metrics, and artefacts; every run reproducible.
• Model Registry: Centralised model versioning with staging, production, and archived lifecycle stages.
• Training Pipelines: Automated model training jobs, scheduled or triggered, with hyperparameter management.
• Model Deployment: Serving endpoints or batch inference pipelines, deployed through CI/CD, not notebooks.
• Monitoring & Retraining: Drift detection, performance alerts, and automated retraining triggers.
• Documentation & Handover: Your team can maintain and extend everything we build, or we continue managing it as a service.

How it works

1. Discovery: Assess current ML workflows, model inventory, and deployment gaps.
2. Architecture: Design the MLOps pipeline: experiment tracking, registry, serving, monitoring.
3. Build: Everything as code: MLflow configuration, training jobs, deployment pipelines, CI/CD.
4. Handover or Operate: Documentation and knowledge transfer; your team takes ownership, or we continue managing the platform as a service.