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Public Cloud · AI & Machine Learning
MLOps Studio
A sovereign, scalable, container-native MLOps platform based on KubeflowTM — enabling organizations to automate ML workflows with portability, reproducibility, and cloud-native orchestration.
- Open-source foundation
- Kubernetes-native
- EU Sovereign
Product Features
A complete environment for the ML lifecycle
A fully managed KubeflowTM based MLOps platform for end-to-end ML lifecycle — from data processing through training, evaluation and deployment — with scalable Kubernetes-native orchestration.
Pipelines — UI, SDK & scheduling
Build, schedule, and manage ML workflows through a visual pipeline UI or Python SDK. CloudFerro manages the underlying KubeflowTM and Kubernetes infrastructure for you.
Portable, container-based workflows
Run every pipeline step in an isolated container on Kubernetes. Reproducible, version-controlled workflows can move across cloud, hybrid, and on-prem environments without major refactoring or vendor lock-in.
Distributed CPU & GPU training
Scale PyTorch, TensorFlow, and JAX training jobs across multiple CPU or GPU nodes with KubeflowTM Training Operator — without manually configuring the cluster.
Experiment tracking, artifacts & lineage
Automatically capture pipeline metadata, artifacts, model versions, and lineage. Compare experiments, reproduce previous runs, and maintain full traceability throughout the ML lifecycle.
Modular ML platform
Extend the environment with only the capabilities your teams need, including model serving, model registry, hyperparameter tuning, and monitoring.
Streamline the entire ML lifecycle
Deliver ML projects faster with lower overhead — using a managed, scalable, and reproducible pipeline platform that works across environments and respects data sovereignty.
Reduced operational overhead
CloudFerro manages KubeflowTM, Kubernetes, infrastructure provisioning, and orchestration. ML teams focus entirely on building pipelines and training models — not on platform maintenance or DevOps.
End-to-end workflow automation & orchestration
Define the entire ML lifecycle — data ingestion through deployment — as reproducible, versioned, container-based pipeline flows. Eliminate manual handoffs between pipeline stages and reduce time-to-production.
Scalable and distributed training
Scale compute elastically across Kubernetes clusters. Run distributed GPU-backed training for large-scale deep learning, EO data processing, and high-throughput ML workloads without capacity planning.
No vendor lock-in
Built entirely on open-source KubeflowTM and Kubernetes standards. Your pipelines, models, and workflows remain portable — no proprietary API dependencies, no migration penalty when requirements change.
Typical Use Cases
What can you build with CloudFerro MLOps Studio?
INTEGRATIONS
Natively integrated with the CloudFerro AI ecosystem
MLOps Studio connects to CloudFerro compute, storage, notebooks, monitoring, and orchestration services — a single coherent platform for the entire ML pipeline lifecycle.
AI Experiments
Track model performance, manage versions, and monitor production deployments post-pipeline using MLflowTM.
AI Compute (GPU/CPU resources)
Elastic GPU and CPU compute powers distributed training jobs and compute-heavy pipeline steps.
Managed Notebooks
Use notebooks for data exploration and prototyping, then promote experiments directly into pipelines.
Object & Block Storage
Dataset storage, pipeline artifacts, model checkpoints, and logs accessible directly from any pipeline component.
Managed Kubernetes
MLOps Studio runs on CloudFerro's managed Kubernetes — providing the compute backbone for pipeline execution and model serving.
Hyperparameter tuning / AutoML
Integration with hyperparameter search and model optimisation across distributed pipeline runs.
Scope description
Technical Specification
Fully managed Kubeflow environment for building, orchestrating, and running end-to-end ML pipelines, container-based, EU-sovereign, and integrated with the full CloudFerro AI stack.
Orchestrator
Pipelines — UI + Python SDK + scheduling
Execution model
Container-based (Docker / Kubernetes)
Training
Distributed CPU/GPU via Kubeflow TM Training Operator
Hyperparameter tuning
Integration with hyperparameter tuning / AutoML tools
Locations
Poland (WAW) · Germany (FRA)
Billing
Usage-based (compute + orchestration + storage)
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.
Other Products
AI & Machine Learning
Workflows
Automate data and ML workflows with a managed orchestration environment hosted in EU data centres.
AI & Machine Learning
AI Experiments
Track experiments, manage model versions and monitor production models in a managed MLFlow environment.
Containers & Serverless
Managed Kubernetes
Deploy production-grade Kubernetes clusters through GUI, CLI or Terraform without managing the control plane yourself.
EO Products & Services
Managed Notebooks
Start working in a managed JupyterLab environment without setup. Use preconfigured ML and data science libraries, choose CPU or GPU-backed notebooks.
AI & Machine Learning
Managed GenAI Services
Use a fully managed, privacy-first GenAI service with OpenAI-compatible endpoints, hosted on 100% European infrastructure with processing in Poland.
Containers & Serverless
Functions
Run event-driven functions, lightweight workflows and scheduled tasks without provisioning servers.