Data and ML platforms
Data and ML platform foundations
Create the smallest dependable data, experiment, evaluation, and deployment foundation for current teams and models.
Primary buyer: Data, ML, and platform engineering leadersThe real constraint
Teams accumulate scripts and model demos without a dependable path from data change to safe production release.
How we approach it
We design contracts and automation around the smallest platform that supports the real model and team lifecycle.
How we prove it
A clean path from source data to evaluated deployment is reproduced with rollback, cost, access, and ownership tested.
Production system
Data and ML platforms
The pipelines, evaluation, deployment, observability, and governance that make model delivery repeatable.
- 01Data ingestion and quality contracts
- 02Experiment and evaluation workflow
- 03Model registry and deployment automation
- 04Observability, drift, and cost controls
- 05Access, lineage, rollback, and ownership
Typical fit
A repeatable path from governed data to evaluated production release
FAQ
Questions before a pilot
These answers describe how we normally work. Only a signed agreement creates commitments. Website enquiry terms
01Do we need a platform, or are we being sold one?
A team often needs less than it is told. If you run one model and deploy it monthly, a pipeline and a checklist beat a platform. We start by asking what actually hurts, whether that is fear of releasing, no reproducibility, or data nobody trusts, and build the smallest thing that removes it.
02Can you work with the stack we already have?
Yes. Replacing a working warehouse or orchestrator to suit our preferences would be a bad trade. We default to Azure because that is where we are deepest, but the more common job is making what you already run observable, reproducible, and safe to deploy from.
03Who runs this after you leave?
Your team, and we build it that way from the start: no bespoke tooling only we understand, a runbook written for whoever is on call, and a handover where someone on your side has personally done a release and a rollback rather than watched us do one. The people who inherit a platform are rarely the people who chose it, so it has to be legible to whoever is actually on call at the time.
Service focus
The smallest platform your team will actually maintain
Ambitious platform projects tend to fail on the team that inherits them. We build the smallest dependable path from source data to an evaluated release, with contracts on the data you depend on, and stop there until something proves it needs more.
- Built for
- Data, ML, and platform engineering leaders
- Designed to achieve
- A repeatable path from governed data to evaluated production release
What you get
- Ingestion with explicit data-quality contracts
- Experiment tracking and a shared evaluation harness
- Environment, access, and release separation
- Runbook written for the team that will own it
Need a different system?
Not quite your solution?
Explore nearby systems or bring the outcome, workflow, and constraints that do not fit a predefined category. Tandemora can scope a custom solution around the real operating problem.
Your version will have different constraints.
Bring the workflow, current system, available evidence, and the assumption you trust least. We will identify the smallest useful next step.
Discuss this system