Data and ML platforms
Production MLOps
Automate model packaging, approval, deployment, rollback, and retraining around explicit quality gates.
Primary buyer: ML and platform engineering teamsThe 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
Faster, safer model changes with visible lineage and ownership
FAQ
Questions before a pilot
01Do we need perfect data before starting?
No. Discovery establishes whether representative data exists, what quality gaps matter, and the cheapest evidence needed before a production promise.
02Can Tandemora own the complete implementation?
Yes. Scope can include product interface, models, data, cloud, integrations, observability, deployment, and handover, with specialists added when the system requires them.
03How is pricing determined?
Uncertain work begins with a bounded paid discovery or pilot. Production is priced after evidence clarifies data, integration, quality, operations, and ownership.
Service focus
MLOps Consulting and Implementation Services
Implement reproducible model packaging, quality gates, deployment, rollback, lineage, monitoring, and retraining for one real model path.
- Built for
- ML and platform engineering teams
- Designed to achieve
- Faster, safer model changes with visible lineage and ownership
Related delivery scope
- MLOps implementation services
- machine learning deployment automation
- model CI/CD pipeline
- Azure MLOps consulting
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