Problem
Our pipelines break and no one knows why.
Reports are always a day behind.
Every new data source is a project.
Outcomes
- Reliable, monitored data flows
- Faster access to trusted data
- A platform your team can operate
Scope
In scope
- Platform design
- Pipeline build and testing
- Monitoring and handover
Not included
- Data strategy and governance (separate capability)
- Business intelligence dashboards (see analytics)
Machine learning engineering
A model that works in a notebook is a long way from one that works in production. We build the pipelines to train, deploy and monitor machine learning models, so they stay accurate as data changes.
- Training and deployment pipelines.
- Model versioning and reproducibility.
- Monitoring for drift and degraded performance, with alerts to a named owner.
Approach
Discovery
We agree the problem, the data and the constraints, and how success will be judged.
Delivery planning
We shape the approach, sequence the work and set out costs, risks and controls.
Build and test
We build in the open, test against real data and cases, and prove it before it goes near production.
Operational readiness, hypercare and closure
We hand over, support the first weeks of use, and confirm the outcome before we close.
Deliverables
Technologies
Controls
Frequently Asked Questions
Do we have to move to a specific cloud?
No. We design for the platform that fits your constraints and record why.
Who runs it afterwards?
Your team, with documentation and runbooks, or us under managed services.
Ready to scope this?
Tell us where your data gets stuck and we will help it flow.