MLOps with security and governance built in

MLOps is the practice of taking a machine-learning model from an experiment on one person's laptop to a path your team can run again: versioned training, a controlled release, monitoring, and a record of what ran and who approved it. SEYA Group builds that path for the models and platform you name, so that a model in production is maintained like any other piece of software.

Who this is for

CTOs and heads of data who have models that work in a notebook and a business that now depends on them. IT managers who have been asked to "productionise" a model and have no agreed way to do it. Data science leads whose team retrains by hand and cannot say which version is live. Solution architects designing the platform those models will run on and wanting the release path designed at the same time.

If the data the models need is not yet collected in one place, start with data platforms and analytics. This page assumes the data exists and the models are the problem.

Problems we solve

• Training that runs on one machine, with dependencies nobody else can reproduce.
• No agreed way to promote a model from experiment to production, so it happens by copying files.
• No record of which model version is serving, what data it was trained on, or who can change it.
• Models that degrade quietly because nothing watches their inputs or outputs.
• Data scientists spending their time on infrastructure instead of on models.

What you get

• Assessment. A review of how models are trained, reviewed, and released today, and a recommended first phase for the gaps that block a repeatable release.
• Platform recommendation. Which managed services or open-source tools your team would operate for experiment tracking, model registry, pipelines, and serving, and what stays outside the first phase.
• Pipeline build. The agreed training, validation, and deployment steps as code, with environments separated and promotion gated by checks you define.
• Monitoring. Tracking of model inputs, outputs, and performance against the measures your team chooses, with alerts routed to the people who own the model.
• Handover notes. The documentation your engineers and data scientists need to run, extend, and audit the pipeline without us.

How an engagement runs

• Scoping call. You describe the models, the platform they run on, and the release problem you most want solved.
• Assessment. We read the current workflow with your data scientists and engineers and write up the gaps and the first phase.
• Build the first path. One model goes through the new pipeline end to end, with your team pairing on the work.
• Extend. Further models follow the pattern, and the checks and monitoring are tuned with your team.
• Handover. Your team runs the pipeline. Ongoing support continues only if the brief says so.

Technical scope

The pipeline can run on AWS, Microsoft Azure, or Google Cloud using that platform's managed machine-learning services, or on open-source tooling, according to what your team already operates and what the brief selects. Infrastructure is defined as code, and models are packaged in containers where serving calls for it. SEYA works with the cloud and tooling you have chosen and recommends open-source options where they fit.

What you need in place

At least one model that matters to the business, the data it trains on in an accessible location, and data scientists and engineers who will own the pipeline afterwards. A cloud account or cluster to run in, which you own. Where the data platform does not yet exist, that is a separate brief under data platforms and analytics, and security scanning of the pipeline is designed under security and DevSecOps.

Out of scope here: building the models themselves, unless the brief includes it; a compliance certification; and running the platform for you after handover, unless agreed. When the work is building the models, our sister company Mars Innovation Technology takes that build. SEYA's scope on this page is how those models are released, monitored, and governed.

Related work

• Browse SEYA case studies across cloud foundations, migration, and delivery pipelines

Questions buyers ask

Do we need to change the tools our data scientists use?

Usually not. The pipeline wraps the training code they already write; the changes are in how it is versioned, run, and promoted. Where a tool change is recommended, the assessment says why.

Who owns the pipeline and the models?

You do. Code lives in your repositories, models in your registry, and the pipeline runs in your cloud account.

How do you work with our data science team?

As engineers who join their workflow. The first model goes through the pipeline with them, so the conventions are ones they chose.

How long does the assessment take?

It depends on how many models and teams are in scope and how well the current workflow is documented. We agree the scope in the first call and flag early if it needs to widen.

Can you help with the data platform as well?

Yes, as a separate brief. Many MLOps conversations start with a data problem, and we will tell you if that is the case here before pipeline work starts.

If a model your business relies on still ships by hand, book an MLOps assessment call and describe how it reaches production today.