MLOps Pipeline Setup
We build the pipelines that take a model from notebook to production and keep it healthy there: versioned data, reproducible training, gated releases and monitoring that catches drift before your users do.
Why teams bring us in
Many teams can train a good model once. Few can retrain it next month, prove it's better, and roll it out safely. Without that loop, every model decays and every update becomes a small project of its own.
We map how models move today from idea to production, including the manual steps nobody has written down. Then we design a pipeline that automates the repeatable parts and adds checks at the points where things go wrong: data changes, training runs that silently degrade, and releases that skip review.
- Reproducible training: pinned data versions, containers and configs for every run
- Model registry: lineage from dataset to deployment, with approval steps
- Evaluation gates: automatic quality, safety and cost checks before promotion
- Progressive delivery: shadow, canary and instant rollback for model releases
- Observability: latency, cost, drift and user-feedback signals in one place
Teams that get the most from this service
Data science teams
Models are trained in notebooks and deployed by hand, and retraining is a project every time.
Platform teams
You are asked to support ML workloads and need a standard, supportable way to do it.
Regulated organizations
Auditors ask how a model was trained and approved, and the answer lives in someone's memory.
Four phases, each with a sign-off.
The result is a Git-driven workflow. A change to data, code or configuration triggers training, evaluation and packaging automatically. Candidate models must pass quality, safety and cost gates before promotion, and releases roll out gradually with automatic rollback if production metrics move the wrong way.
| Phase | What happens | You receive |
|---|---|---|
| Map | Current workflow, tools, pain points | Target pipeline design |
| Build | CI/CD for models, registry, feature store | Pipelines in your Git |
| Gate | Eval suites and promotion rules | Automated release checks |
| Observe | Dashboards, alerts, retrain triggers | Monitoring + on-call guide |
What you keep when we're done.
End-to-end pipeline
Data → train → evaluate → deploy → monitor, defined as code and triggered from Git.
Evaluation suite
Quality, safety and regression tests that run on every candidate model.
Monitoring stack
Dashboards and alerts for drift, latency, cost and failure rates.
Tools we use
Chosen per client. We work with what your team already runs wherever it makes sense.
Questions about MLOps Pipeline Setup
Do we need Kubernetes?
Not always. For smaller teams we can build on managed services. For larger or on-premises environments Kubernetes is usually the most supportable base.
Will this work with LLMs as well as classic ML?
Yes. The same pipeline handles prompt and retrieval changes, fine-tuning runs and traditional models, with evaluation suites suited to each.
How long until the pipeline is useful?
The first automated path, from a Git change to a deployed model with an evaluation gate, is usually running within three to four weeks.
Often paired with
Private & On-Prem LLM Deployment
Run open-weight models on your own servers or private cloud, with no data leaving your network.
Explore service →SVC-02GPU Infrastructure Optimization
Cut inference and training spend with better scheduling, quantization and right-sized clusters.
Explore service →SVC-03Cloud-to-AI Migration
Move legacy workloads and data platforms onto infrastructure that is ready for AI.
Explore service →Talk to an engineer about MLOps Pipeline Setup.
A 45-minute call with an engineer, not a sales script. You'll leave with a rough architecture, a cost range and the next three steps.