Training pipelines
Reproducible, automated training with data and experiment versioning.
Services
MLOps Services
Industrialize the ML lifecycle so models stay accurate, governed, and cost-effective in production.
- 10×faster model-to-production cycles
- 100%of models versioned and reproducible
- <90srollback when a model misbehaves
Capabilities
Model lifecycle, industrialized
Operate ML models at scale with reliable training, deployment and monitoring.
Model serving
GPU-efficient inference with autoscaling, batching and failover.
Drift monitoring
Input and performance drift detected before it hits business metrics.
Feature stores
Consistent features between training and serving, governed and discoverable.
How we deliver
From notebook heroics to release discipline
Models that matter deserve the same rigor as production code — versioned, tested, gated, observed. We install that rigor without slowing your scientists.
Lifecycle
Every model reproducible, every release gated
Data, code and config versioned together; evaluation gates before promotion; canary rollouts and instant rollback after — the boring machinery that makes model changes safe.
- Registry linking model, data and code versions
- Eval gates and champion/challenger promotion
- One-command rollback with full provenance
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Serving
Inference that meets its SLO and its budget
Right-sized serving with batching, quantization and autoscaling — classical models and LLMs on one operational surface, with utilization you can defend.
- vLLM and Triton serving tuned per workload
- Autoscaling matched to traffic patterns
- Unified ops across self-hosted and API models
Private VPC
GPU poolAutoscaleMulti-AZ
AWSAzureGCPOn-prem
Vigilance
Drift caught before customers catch it
Input distributions, prediction quality and business KPIs monitored continuously; retraining pipelines trigger on thresholds and promote only with proven lift.
- Data and concept drift detection with alerting
- Automated retrain-evaluate-promote loops
- Model performance tied to business metrics
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Use cases
MLOps engagements we run
Platform builds
End-to-end MLOps on your cloud ML stack.
LLMOps extensions
Prompt and eval discipline added to classic MLOps.
Serving optimization
Latency and GPU cost cut on existing endpoints.
Governance retrofits
Reproducibility and approvals for regulated models.
Drift programs
Monitoring and retraining loops for model fleets.
Migration projects
Models moved to modern serving without downtime.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
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1
Align
Goals, constraints, success metrics and stakeholders.
-
2
Design
Architecture, delivery plan and risk controls.
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3
Deliver
Build, integrate, harden and launch.
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4
Operate
Monitor, optimize cost and continuously improve.
Model deployments used to be quarterly events with war rooms. Now they're Tuesday afternoons — and the one bad model we shipped was rolled back in ninety seconds.
Works with your stack
Built on your ML platform
We extend SageMaker, Vertex, Azure ML or open-source stacks — not replace them.
- MLflow
- Kubeflow
- SageMaker
- Vertex AI
- Azure ML
- Ray
- vLLM
- Feast
- Airflow
- Kubernetes
- Terraform
- Grafana
FAQ
Common questions
We have SageMaker/Vertex. Why do we need you?
The platform is the toolbox, not the discipline. We wire the gates, registries, monitoring and rollback paths into your team's actual workflow — that's what turns tooling into reliability.
Does MLOps cover our LLM work too?
Yes — prompts, retrieval indexes and eval sets join models as versioned artifacts, with eval-based gates. One release discipline across classical ML and LLMs.
How disruptive is adoption for data scientists?
Minimal by design: scientists keep their tools; the platform meets them at the point of promotion. The pitch that lands: fewer 2am pages, faster paths to production.
Can you meet model-risk-management requirements?
Yes — reproducibility, approval workflows, documentation and audit trails are exactly what MRM wants, generated by the platform rather than written by hand.
Next step
Make model releases boring
We'll assess your model lifecycle and fix the riskiest gap first.