A.6 · Artificial Intelligence & Machine Learning
MLOps & Machine Learning Platform Engineering
From notebook to production, with a path back when it goes wrong
What we build
The platform that lets a team ship models repeatedly rather than once. Feature stores with training and serving parity, experiment tracking and model registries, reproducible training pipelines, CI and CD for models with automated evaluation gates, canary and shadow deployment, GPU scheduling and cost control, and monitoring for data drift, concept drift, and quality regression in production.
Capabilities
- Feature stores with training and serving parity, so production matches the experiment
- Experiment tracking, model registry, and lineage from a prediction back to its data
- Reproducible pipelines, so a model trained in June can be rebuilt exactly in December
- Automated evaluation gates in CI, with canary and shadow deployment before full rollout
- GPU scheduling, quota, and cost attribution per team and per model
- Drift and quality monitoring in production, with rollback that is rehearsed
Related services
How it connects
Where it sits in the stack.
This service, and the two it hands off to. None of them can be optimised alone.
MLOps Platforms
The platform that lets a team ship models repeatedly rather than once.
Generative AI & LLMs
Production systems built on large language models, hosted or self-hosted.
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Multi-step agent runtimes wired into the systems an organisation actually runs on.
AI & Machine Learning · see serviceBring us the whole problem.
Tell us where the work is stuck, whether that is a model that never reached production, an application nobody can change, a data platform nobody trusts, or a plant the business cannot see. An engineer replies with a first read, not a sales deck.