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PUBLIC CLOUD · AI & MACHINE LEARNING
AI Experiments
A managed MLflowTM service for tracking experiments, managing model versions, and monitoring models in production.
- Open-source MLflowTM
- No vendor lock-in
- EU Sovereign
Product Features
Experiments, models, and monitoring in one environment
Track, version, and monitor ML models with a sovereign, fully managed MLflowTM environment.
Fully managed MLflowTM environment
Track experiments, metrics, parameters, models, and artifacts in a production-ready MLflowTM environment managed end-to-end by CloudFerro — without deploying or maintaining the underlying infrastructure.
Experiment tracking & reproducibility
Compare runs across parameters, metrics, datasets, and model versions. Reproduce previous experiments using stored metadata, artifacts, and complete run history.
Model lineage & artifact management
Track the relationships between datasets, training runs, model versions, and generated artifacts to support traceability, governance, and auditing across ML projects.
Model registry & lifecycle management
Version, register, and promote models through development, staging, and production. Maintain a clear history of model changes, approvals, and lifecycle stages.
Production model monitoring
Capture model inputs, outputs, predictions, and performance metrics in production. Detect changes in model behaviour and use logged data for debugging, auditing, and retraining.
Stay in control of experiments and ML models
CloudFerro delivers continuous oversight of production AI — ensuring reliability, compliance, and auditability without the cost or complexity of running ML monitoring infrastructure in-house.
Continuous monitoring & drift detection
Catch model degradation before it affects your users. Automated drift alerts and retraining triggers keep production AI reliable — without engineers monitoring dashboards manually.
Full model lifecycle management
Move models confidently from experiment to staging to production. Versioned registry, promotion workflows, and one-click rollback give your team full control without operational overhead.
Strong reproducibility & auditability
Every run, artifact, and model version is stored with full lineage. Reproduce any historical experiment exactly and produce complete audit trails on demand — for internal reviews or external audits.
Ideal for regulated industries
Finance, healthcare, and public sector teams get the experiment lineage, model versioning, and audit trails required to demonstrate compliance — all in a single managed platform.
Typical Use Cases
How can you use CloudFerro AI Experiments?
INTEGRATIONS
Natively integrated with the CloudFerro AI ecosystem
AI Experiments connects to CloudFerro pipelines, notebooks, compute, and storage — a unified layer of observability across the entire ML lifecycle.
MLOps Studio
Log pipeline metrics and artifacts to MLOps Studio, trigger retraining from drift alerts, and promote outputs to the model registry.
AI Compute (GPU)
GPU training jobs log metrics and model checkpoints to AI Experiments automatically — full tracking of distributed runs.
Managed Notebooks
Log experiments and artifacts directly from notebook cells — from interactive exploration to tracked, reproducible experiments.
Object Storage
Model artifacts, prediction logs, and run outputs stored in CloudFerro Object Storage — accessible from any pipeline component.
Managed Kubernetes
Deploy monitored models to Kubernetes and feed live prediction logs back to AI Experiments — closing the deployment-to-monitoring loop.
Scope description
Technical Specification
Fully managed MLflowTM Tracking Server and Model Registry — with real-time monitoring, drift detection, prediction logging, and automated retraining triggers. EU-sovereign and GDPR-aligned.
Tracking Server
Managed MLFflow TM— experiments, runs, metrics, parameters, artifacts
Model Registry
Versioning · lineage · staging · production promotion workflows
Monitoring
Real-time performance metrics · drift detection · prediction logs
Alerts & triggers
Automated retraining triggers via MLOps Pipelines
Locations
Poland (WAW) · Germany (FRA)
Billing
Usage-based (compute + storage + API requests)
FAQ
Frequently Asked Questions
Didn’t find the answer you were looking for? Get in touch if you need more information or would like to speak with our team.
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