From notebook to production, every time.
MLOps consulting and platform engineering: experiment tracking, model registry, training pipelines, GPU scheduling and model serving with MLflow, Kubeflow, Ray and vLLM, in your cloud or on-prem.
Models rarely fail in the notebook. They fail in the hand-off to production: unversioned data, manual deploys and GPUs sitting idle.
MLOps platform, end to end.
Experiment tracking
Runs, parameters and metrics recorded with MLflow so results are reproducible.
Model registry
Versioned models with stages, approvals and lineage.
Training pipelines
Kubeflow or Airflow pipelines that retrain on schedule or on new data.
GPU scheduling
Shared GPU clusters with queues and quotas, so expensive cards stay busy.
Model serving
vLLM, KServe or Ray Serve behind a stable API, with autoscaling.
Monitoring
Latency, cost per request and model drift tracked in production.
Three steps, no big-bang cut-over.
Assess
How models are trained, shipped and served today.
Build
Tracking, registry, pipelines and serving on your infrastructure.
Operate
Platform run by your team or by us.
Deliverables you keep.
- MLOps platform architecture
- MLflow tracking and registry
- Training pipelines
- GPU cluster with quotas
- Model serving with autoscaling
- Cost and drift dashboards
Tools we use here.
Start small, or hand it all over.
Assess
Read-only review, ranked plan in 5 days.
Build & hand over
Built in your account, then handed to your team.
Fully managed
We run it end to end, with an SLA.
Support
Senior help for your engineers.
Questions about MLOps platform.
Do we need a full MLOps platform?
Not always. Small teams often need just tracking, a registry and one-click deploys; we build only what you will use.
Cloud or on-prem GPUs?
Either, or both. Owned GPUs for steady training and serving, cloud for peaks.
Can you serve LLMs as well?
Yes. We deploy open models with vLLM and measure cost per 1M tokens against API pricing.
Does this work with SageMaker or Vertex AI?
Yes. We can integrate with them or move workloads off them when self-hosting is cheaper.
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