Services
MLOps & Model Deployment
Get models out of prototypes and into production, then keep them there. We build CI/CD and continuous-training pipelines, automate testing, validation and rollback, and set up serving for batch, real-time and streaming inference — on-prem, in your VPC or in the cloud.
We take models from prototype to production with CI/CD and continuous-training pipelines, automated testing and rollback, and serving that scales — so every release is repeatable and safe to change.
What's included
- Production deployment for batch, real-time and streaming inference
- CI/CD and continuous-training (CT) pipelines
- Automated packaging, testing, validation and rollback
- Scalable serving for ML and GenAI workloads
How it works
A clear path from the first conversation to a system your team runs.
- Step 1:
Assess your path to production
We review how models reach production today, and where releases stall or break.
- Step 2:
Design the pipeline
CI/CD and continuous-training pipelines, deployment patterns and rollback, sized to your stack.
- Step 3:
Automate build, test and release
Packaging, validation and release gates run on every change, with canary rollouts for models.
- Step 4:
Hand over runbooks
Your team gets the pipelines, dashboards and runbooks to operate and extend them.
Technologies we work with
Chosen for your environment: we work in your cloud and with the tools you already use.
MLOps platforms
- PyTorch
- TensorFlow
- Vertex AI
- MLflow
- Kubeflow
- Prometheus
- Grafana
CI/CD
- GitHub Actions
- GitLab CI/CD
- Jenkins
Containers and orchestration
- Kubernetes
- Docker
- OpenShift
In practice
HealthcareHealthcare MLOps: Production-Ready ML Pipelines
Healthcare ML models stalled on the way from prototype to production.
Representative engagement — figures are illustrative of the outcomes this approach is designed to deliver, not audited client results.
Read the case studyFrequently Asked Questions
Can you deploy on our own infrastructure?
Yes. Pipelines and serving can run on your Kubernetes or OpenShift cluster, in your VPC, or fully on-prem — including air-gapped setups — with SSO, RBAC and private networking.
What is continuous training, and do we need it?
Continuous training retrains and re-evaluates a model automatically when new data arrives or quality drops. We recommend it where data changes fast; otherwise scheduled or on-demand retraining is simpler.
Does this cover LLM and GenAI applications?
The same release discipline applies. For prompt versioning, evaluation gates and hallucination monitoring, see our Generative AI & LLMOps service.
Can you work with our existing tools?
Yes. We build on what you have — GitHub Actions, GitLab CI/CD or Jenkins; MLflow, Kubeflow or Vertex AI — rather than forcing a new stack.
How is an engagement scoped?
We start with a discovery call and a short assessment of your current path to production, then agree a fixed scope, milestones and hand-over before any build work begins.