10 million metrics. Processed daily.
Run ML pipelines and analytics workloads across distributed compute with automatic optimisation and cost savings — the same stack we run ourselves.
Deploy it in one file
Core handles multi-cloud orchestration, load balancing and failover automatically. Everything below is real config.
jobs:
- name: ml-training-pipeline
image: python:3.11
script: |
python train_model.py \
--data-path /mnt/data \
--output /mnt/models
parallelism: 10
resources:
cpu: 8
memory: 32GB
gpu: nvidia-t4
providers:
- gcp: us-central1
- dfc: compute-pool-01
- akash: gpu-cluster
scheduling:
strategy: cost-optimized
retry_policy: exponentialBuilt for production workloads
Distributed processing
Process millions of data points in parallel with automatic job orchestration, retry logic and fault tolerance.
- Automatic parallelisation
- Dynamic resource scaling
- Fault-tolerant execution
- Job dependency management
ML pipeline orchestration
Define complex workflows in real Python with automatic scheduling, resource allocation and dependency resolution.
- Python-native workflows
- Automatic retry logic
- GPU resource management
- Model versioning
Cost-optimised placement
Intelligent workload placement across GCP, the DigitalFrontier Edge and Akash based on cost, performance and availability.
- 35% average cost savings
- Spot instance support
- Multi-cloud bidding
- Real-time cost tracking
Real-time dashboards
Sub-2s query response times with distributed caching and multi-region deployment.
Data pipeline integration
Native connectors for S3, GCS, BigQuery and major data warehouses.
Model serving
Deploy trained models as auto-scaling inference endpoints.
Experiment tracking
Built-in MLflow integration for experiment tracking and model registry.
Ready to get started?
Deploy on infrastructure we run ourselves, governed entirely by EU law.