Valfort Tech
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MLOps consulting that gets models into production
MLOps consulting is the work of building the pipelines, registry, release process and monitoring that move machine learning models from a notebook to production reliably. Valfort Tech's senior engineers set up training pipelines, a model registry, canary releases with rollback, drift monitoring and GPU scheduling, all as code in your repositories, so model releases go from months to days.
When teams call us about MLOps
Models wait months for a path to production.
- Trained models wait weeks or months for someone to build a path to production.
- Nobody can say which data, code and parameters produced the model that is live today.
- A bad model release means a manual rollback and a long night for the team.
- Model quality drops in production and you find out from customers, not dashboards.
- GPUs sit idle half the day while training jobs queue behind each other.
What we deliver
Everything ships as code in your repositories, with runbooks and a recorded handover.
Training pipelines
Reproducible training pipelines on Kubeflow or Ray, triggered from CI, with data, code and parameters versioned for every run.
Model registry
An MLflow model registry with clear stages and approvals, so every production model traces back to the run that built it.
Canary releases and rollback
Models served on KServe behind canary releases managed by Argo CD. A failing model rolls back automatically, not by hand.
Drift and quality monitoring
Metrics for data drift, prediction quality and latency, exported through OpenTelemetry with alerts routed to the right owner.
GPU scheduling
Kubernetes node pools, quotas and queueing for GPU workloads, so training and inference share capacity without waste.
Runbooks and handover
Written runbooks for releases, rollbacks and incidents, plus a recorded handover so your team runs the platform without us.
Typical results
Teams typically go from months of manual work per model to shipping a new version in a few days.
With canary releases in place, a bad model is typically pulled from production in minutes rather than hours.
Shared scheduling and right-sized node pools typically cut idle GPU time and the cloud spend that comes with it.
Typical ranges, not promises. We measure your own baseline in the first week.
How a MLOps engagement runs
The first two weeks run on read-only access. Every change after that goes through your pull requests, with one named engineer and a written update every Friday.
- Week 1
- Read-only reviewWe review your training code, data flow, serving setup and cloud accounts on read-only access, and map where releases stall.
- Week 2
- Roadmap and first fixYou get a scored report and a ranked 90-day roadmap, and we ship one fix to production through your pull requests.
- Weeks 3–6
- Pipelines and registryWe build versioned training pipelines and the model registry, then move your first model through them end to end.
- Weeks 7–10
- Releases, monitoring, handoverWe add canary releases, drift monitoring and GPU scheduling, then hand over runbooks and a recorded walkthrough.
Tools we work in
We work in the tools you already run, and only add new ones when they earn their place.
- Kubernetes
- Terraform
- AWS
- GCP
- MLflow
- Kubeflow
- Ray
- KServe
- Argo CD
- OpenTelemetry
MLOps questions
What does an MLOps consultant actually do?
An MLOps consultant builds the systems that take a trained model to production and keep it healthy there. At Valfort Tech that means training pipelines, a model registry, canary releases with rollback, drift monitoring and GPU scheduling. Everything is delivered as code in your repositories, with runbooks and a recorded handover, so your team owns it when we leave.
How much does MLOps consulting cost?
Engagements start with a 10-day readiness sprint at a fixed $3,500. You get a scored report, a ranked 90-day roadmap and one fix shipped to production. If we do not find at least three times the fee in savings or avoided risk, the sprint is free. Follow-on work is scoped after the sprint, and the sprint fee is credited to it.
How long does it take to set up MLOps?
A working MLOps foundation typically takes 6 to 10 weeks after the readiness sprint, depending on how many models and environments you run. The first two weeks run on read-only access while we review and plan. Pipelines and the registry come next, followed by canary releases, monitoring and a recorded handover to your team.
Should we use MLflow, Kubeflow or a managed platform like SageMaker?
It depends on your team, your cloud and how many models you run, and we recommend a fit during the readiness sprint. Many teams pair MLflow for tracking and the registry with Kubeflow or Ray for pipelines on Kubernetes. We work with what you already have where it makes sense, rather than forcing a rebuild.
Will we depend on Valfort Tech after the project ends?
No. Every pipeline, policy and dashboard lives as code in your own repositories, and every change goes through your pull requests. We write runbooks for the common tasks and record a handover session for your engineers. You keep one named engineer and a written update every Friday while we work, and full ownership afterward.
Not sure where to start?
Book a 30-minute call. We'll tell you whether the readiness sprint, a single service or nothing at all is the right next step.