We build the engineering around machine learning models: reproducible training pipelines, versioned artifacts, automated tests and deployment, and monitoring that tells you when a model has stopped doing its job.
What we build
- Training pipelines. Data extraction, validation, feature computation, training and evaluation expressed as code that runs the same way on a laptop, in CI and on a schedule.
- Experiment tracking. Parameters, data versions, metrics and artifacts recorded so any past result can be traced and reproduced.
- Model registry and promotion. A single place where versions are stored with their metadata, and an explicit process for moving a model from candidate to production.
- CI/CD for models. Tests for data and feature code, evaluation gates that block a worse model from shipping, and automated builds of serving images.
- Serving. Batch scoring and online inference, with canary or shadow rollouts and a fast rollback.
- Monitoring. Input and prediction drift, data quality checks, latency and error rates, and, where labels arrive later, tracking of real-world performance.
If your system is built around language models, many of the same ideas apply with a different emphasis; see LLMOps.
How an engagement runs
- Assess the current path to production. We trace how a model goes from notebook to live today, where it is manual, and where it has broken before.
- Agree the target. A lightweight design covering environments, artifact storage, approval steps, and the failure modes to monitor.
- Make training reproducible. Pin dependencies, version data references, and move code out of notebooks into tested modules.
- Automate build, test and promote. A pipeline that trains or retrains, evaluates against a fixed benchmark and the current production model, and registers the result.
- Deploy safely. Containerized serving, staged rollout, alerts and a documented rollback.
- Monitor and hand over. Dashboards and alerts tied to actions someone is responsible for, plus runbooks and training for your team.
Principles we apply
- Start small. A team with one model does not need a platform. We add only the pieces whose absence is currently costing you.
- Prefer open, portable tools. Standard formats and open-source components reduce lock-in, and managed cloud services are fine where they save real effort.
- Gate on evidence. No promotion without an evaluation against the incumbent on a fixed set.
- Plan for deletion and retention. Training data, features and logs may contain personal data, so lineage matters. See data mapping and discovery and data subject request automation.
Typical stack
- Python, with MLflow or a comparable tracker and registry; DVC or lakehouse table versions for data references.
- Orchestration with Airflow, Dagster, Prefect or Kubeflow Pipelines, depending on what you already run.
- Docker and Kubernetes for serving, with FastAPI or an inference server in front of the model.
- Terraform for infrastructure and GitHub Actions for CI/CD; see DevOps.
- Prometheus and Grafana, or your cloud's monitoring, plus statistical drift checks on inputs and outputs.
Deliverables
- Pipeline code, infrastructure definitions and CI configuration in your repositories.
- A registry setup with documented promotion rules.
- Monitoring dashboards, alert rules and runbooks.
- An architecture note explaining the design and the reasons for it.
- A handover and training session for the people who will operate it.
What good looks like after the engagement
Success for this work is quiet. A data scientist merges a change and an evaluation runs automatically. The pipeline reports that the new model is better on the fixed benchmark, or that it is not and why. Promotion needs an explicit approval, the serving image is built from the same commit, and a canary release limits the blast radius if something is off. When input data changes shape, an alert reaches a named person with a runbook that says what to check.
We also aim for the opposite of a heroic setup that only its author understands. Every component has documentation, the configuration lives in version control, and a new engineer can read the repository and the architecture note and run the pipeline end to end in a staging environment. If a tool is not earning its keep, we say so and remove it, because every extra moving part is something your team must operate.
Finally, we are candid about scope. Some teams need only reproducible training and a deployment script. Others need scheduled retraining, feature storage and multi-model serving. We start at the smaller end and grow with evidence of need.
Frequently asked questions
We only have one or two models. Is MLOps overkill?
Full platforms are, but the basics rarely are: version control, reproducible training, a tested deployment path and monitoring. We scope to your size.
Do you require a specific cloud?
No. We work on the major clouds and on your own Kubernetes or servers.
Can you work with models our data scientists already built?
Yes. Productionizing existing models is a common starting point, often alongside machine learning improvements.
How do you detect that a model has gone stale?
By monitoring input and prediction distributions, and by measuring against labels when they become available. Which signals matter depends on your problem, and we choose them with you.
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