DevOps and MLOps
The engineering that separates a model that works on a laptop from a model that holds up in production.
DevOps and MLOps
Training a model is the visible part. What eats time and budget is everything else: reproducing that training six months later, serving inference at acceptable latency, understanding why quality dropped, and not burning a fortune on idle GPUs.
That is the layer we build — for your own models and for integrations with third-party ones — so that running it does not depend on one person's memory.
Training pipelines are described in code, with datasets and models versioned. Any experiment is reproducible and comparable with previous ones, which ends the argument about which version was the good one.
On the inference side we handle GPU sizing, autoscaling, caching of repeated requests and cost ceilings. And we instrument all of it: latency, error rate, cost per request and quality drift over time.
What we do
- Reproducible training pipelines and dataset versioning
- Model registry and promotion between environments
- Inference serving with autoscaling and caching
- GPU management and cost optimisation
- Monitoring of latency, cost, errors and quality drift
- CI/CD for applications and for models, with rollback
Key benefits
- Move from prototype to production without a rewrite
- GPU costs visible and capped
- Roll back to the previous model version in one command
- Know that quality dropped before the customer complains
- Environments recreatable from code
When it makes sense
- Shipping a model that today only runs in a notebook
- Cutting the inference bill of a product already launched
- Automating retraining as new data arrives
- Adding staging and rollback for model releases
- Moving GPU workloads to an EU region
Technologies we use
Kubernetes · Docker · Terraform · MLflow · AWS
Why devcave
We do classic DevOps and MLOps in the same team. There is no infrastructure vendor and model vendor blaming each other when something breaks at three in the morning.
From first call to a team shipping
1
Intro call
Thirty minutes on a video call to understand the problem, the context and the deadline. We leave with a clear picture of what is needed and what is not.
2
Proposal and team
We put the scope, the cost, the schedule and exactly who will work with you in writing. No anonymous CVs, no teams that change halfway through.
3
Build
Short iterations with regular demos in your time zone. You see the product working early and adjust before changes get expensive.
4
Launch and support
Production deployment, documentation, handover of all access and ongoing support. The code and the infrastructure stay in your name.
From first call
to a team shipping
in two weeks
Tell us what you need. We reply within 24 working hours, with no strings attached.