PLATFORM CAPABILITY · BUILD ON TOP
Analytics & MLOps
Full ML lifecycle: development, training, deployment, monitoring, on top of governed data products. No parallel pipelines.
Feature engineering on raw extracts, training pipelines separate from the governed data layer, model outputs disconnected from business definitions. When a regulator asks which data a model was trained on, the answer requires weeks of forensic reconstruction. When source data changes, ML pipelines break silently. The data science team maintains a parallel infrastructure that duplicates effort and multiplies governance risk.
One
governed data foundation for ML models, BI and AI agents, no separate pipelines
Full
model lineage from training data to prediction output, auditable throughout
Zero
separate MLOps tooling required. The full lifecycle runs inside the governed platform.
WHAT IT DELIVERS
ML built on trusted data. No duplicate pipelines.
Model development, training and deployment happen on top of Governed Data Products. The data used for training is the same data the business relies on: traceable, quality-validated and semantically defined.
Development directly in Stratio or from your preferred local tool, deployment into production pipelines and continuous monitoring, all within the same governed environment. No separate MLOps tooling required.
Models are governed by the same policies that govern the data they were built on. Lineage extends from raw source to model prediction. Compliance teams can trace any model output to its training data source, without reconstructing pipelines.
Data engineers do not build separate pipelines for model training. Models consume governed data products directly. When data products are updated, model inputs update automatically, with no synchronisation and no duplication.
From data product to production model in three steps.
01
Data scientists access governed data products with clear semantic definitions, then develop code and run experiments using Jupyter workbenches, Stratio MLProjects (the platform’s project structure for developing, versioning and packaging models), their preferred local IDE, or Stratio Cowork (Stratio’s agentic development environment).
02
Models are deployed into production pipelines within the governed environment. Access policies from the data layer extend to model serving. Deployment is versioned and auditable. Rollback is immediate.
03
Evaluate model performance and monitor results, version every deployment and redeploy from the same governed data pipeline, with no re-setup.
Watch how a data scientist accesses governed data products, develops and runs experiments, and deploys a model into production, all within the same governed environment and with no parallel pipelines.
Built for regulated sectors
Fraud detected and prevented in real time, not after the fact.
Compliance validation reduced from weeks to hours.
Automated regulatory reporting at scale, fully auditable.
Grid, assets and the energy transition.
Fraud detected and prevented in real time — not after the fact.
Compliance validation reduced from weeks to hours.
Automated regulatory reporting at scale, fully auditable.
Grid, assets and the energy transition.
Analytics & MLOps in practice
AI forecasting on governed data, with end-to-end explainability and lineage.
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−10%
Logistics cost reduction in less than 6 months
We can show you how Analytics & MLOps integrates with existing ML stacks across regulated environments in Europe, UK and LATAM.
