MLOps & Model Serving
Models that leave the notebook: repeatable training, safe releases and serving that scales with demand.
The challenge
Sound familiar?
A model works in a notebook but nobody can retrain it the same way twice. Releasing a new version means copying files by hand, GPUs sit idle and cost money, and nobody notices when predictions slowly get worse.
Our approach
What we do
- 01
Repeatable pipelines
Data preparation, training and evaluation as code, with every run recorded — data version, parameters, metrics and the resulting model.
- 02
Model registry and releases
Versioned models promoted from staging to production through Git, with a one-step rollback to the previous version.
- 03
Serving on GPU and CPU
Inference endpoints on Kubernetes with GPU node pools that scale down when idle, batching and autoscaling for real traffic.
- 04
Monitoring in production
Latency, errors and cost per request next to quality and drift signals, with alerts before users notice a problem.
What you get
- Training and evaluation pipelines in your repository
- A model registry with promotion and rollback
- Autoscaled inference endpoints
- Dashboards and alerts for model health and cost
Technologies
Let’s talk about your project
A few lines about your situation are enough to start.