InferenzService
MLOps & Model Serving
A model in a notebook isn't a product. MLOps is the engineering that gets models into production, keeps them fast and affordable, and tells you when they start to degrade. We set up the pipelines, serving and monitoring so your team can ship and improve models with confidence.
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What we deliver
- Reproducible training pipelines with experiment tracking and model registry
- CI/CD for models: automated tests, evaluation gates, canary and shadow releases
- Low-latency serving with autoscaling and cost controls
- Monitoring for data drift, quality, latency and spend
- Feature stores and data validation
- Retraining triggers and rollback procedures
Where it's used
Moving a notebook prototype to production
Reducing inference cost and latency
Auditing and monitoring models already in use
Standardising ML delivery across teams
How we work
- Discover. We look at your data, define success metrics and agree what "good" means before building anything.
- Build. We start with a baseline, iterate with measured evaluation, and keep you in the loop with working demos.
- Ship & monitor. We deploy with tests and rollback plans, then monitor quality, cost and drift.
Tools we use
KubernetesMLflowTritonKServeRayAirflowPrometheusTerraform
Questions
Can you work with our existing cloud?
Yes. We build in AWS, Google Cloud or Azure, or on premises, using your accounts and your security controls.
How do you decide when to retrain?
From monitoring signals such as drift and quality metrics, combined with a schedule that matches how quickly your data changes.