ML Ops2025
ML OPSService
Machine Learning Deployment
We don’t see LLMs as hammers looking for nails. Where traditional ML produces better results, we provide:
- 99.9% serving uptime
- 4 wks notebook to production
- 0 unmonitored models
Service details
From a notebook that works to a service that keeps working
Working once in a notebook and working every day under real traffic are different problems. We wrap your model in serving, versioning, drift monitoring and a rehearsed rollback before it ever meets a user.
Serving infrastructure
Models behind a versioned endpoint with autoscaling and sane timeouts.
Reproducible pipelines
Training runs that can be rebuilt months later from the same inputs.
Drift monitoring
Input and prediction drift surfaced before accuracy quietly degrades.
Safe rollout
Shadow, canary and rollback so a bad model never reaches everyone.
Feature consistency
Training and serving share one definition, closing the skew gap.
Cost control
Batching, caching and right-sized hardware, with spend tracked per model.
Rollback rehearsed before it is needed
Shadow traffic, then canary, then full. Drift monitors flag degradation before users feel it, and the path back to the previous model is tested rather than assumed.
- Shadow and canary rollout built in
- Input and prediction drift monitored
- Training and serving share one feature definition
How we work
How we work
Fixed-scope engagement. Shadow traffic running by week two.
- WEEK 1
Audit
We review the model, the data path and what production will actually demand of it.
- WEEKS 2-3
Wrap
Serving, versioning and observability built around the model as it stands.
- WEEKS 4-5
Roll out
Shadow traffic first, then canary, with rollback rehearsed before it is needed.
- WEEK 6
Operate
Monitoring, retraining triggers and on-call handed to your team.
Related projects
Models we’ve put in production
Our model already works — why is this needed?
Working once on a laptop and working every day under real traffic are different problems. This covers serving, drift, rollback and cost.
Can you work with our existing cloud?
Yes. We deploy into the cloud and tooling you already run rather than introducing a new platform.
What happens when accuracy drops?
Drift monitors flag it before users feel it, and retraining triggers plus rollback are wired up in advance.
Do you retrain models for us?
We set up the pipeline and triggers, and can run it or hand it to your team — whichever you prefer.
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