MLOps that keeps production models versioned, monitored and safe to retrain

DevOps automates building, testing and releasing application code, while MLOps applies that discipline to machine learning, where behaviour depends on data as well as code. That adds data versioning, training pipelines, a model registry, evaluation gates before release and monitoring for drift and accuracy, not only uptime. In practice MLOps reuses your DevOps tooling, such as CI/CD, containers and infrastructure as code, and extends it for models.

You need MLOps once a model influences real decisions and will need updating, which is usually earlier than teams expect. Warning signs include models nobody can reproduce, retraining that depends on one person, releases done by copying files and no alert when accuracy drops. With one or two models a lightweight pipeline, registry and monitoring is enough. A fuller platform makes sense when several teams or many models are involved.

We detect drift by comparing live inputs and predictions with the training data and recent history, and by measuring real accuracy once outcomes arrive. Statistical checks flag shifts in feature distributions, missing values and prediction patterns, while delayed ground truth such as actual sales or confirmed fraud shows true performance. Alerts go to a named owner with a runbook explaining whether to investigate the data source, retrain or roll back.

There is no universal interval; retrain when data or performance evidence calls for it, or on a schedule that matches how quickly your domain changes. A demand forecast may benefit from weekly retraining, while a document classifier can stay stable for months. We usually combine a regular schedule with drift-triggered retraining, and every retrained model must beat the current version on a held-out test before it is promoted.

We release new versions as containers behind a stable API and shift traffic gradually, so the previous version keeps serving until the new one proves itself. Depending on risk, a candidate first runs in shadow mode, scoring live requests without affecting users, then takes a small share of traffic as a canary. If latency, errors or prediction checks breach limits, traffic returns automatically to the earlier version.

We choose tools to fit your existing platform rather than installing a new stack by default. Typical building blocks are Python pipelines, Docker containers, Kubernetes with Helm and Argo CD for deployment, GitHub Actions or GitLab CI for automation, Terraform for infrastructure and Prometheus with Grafana for monitoring. Experiment tracking and the model registry use an open-source option or your cloud's managed service, whichever your team will actually maintain. Where you already use managed machine learning services on AWS, Azure or Google Cloud, we build around them instead of replacing them.

Yes, and for most teams that is the right place to start. We refactor the existing training code into a reproducible pipeline, separate configuration from code, add data validation and automated evaluation, register the model artefact and deploy it behind an API with monitoring. Your data scientists keep working in familiar tools, while the path from experiment to production becomes repeatable, reviewable and no longer dependent on one person.

Yes, with a different emphasis. LLM applications usually call a hosted model rather than train one, so the focus moves to versioning prompts, retrieval settings and model choices, running evaluation sets before each release, tracing requests, and tracking cost and latency. The release discipline stays the same: every change is tested against agreed cases, rolled out gradually and reversible if quality drops.

Taking one production model through versioning, automated training, deployment and monitoring is typically a focused first phase of a few weeks after discovery. The main drivers are how many models you run, your current infrastructure and how much existing training code needs refactoring. Onboarding further models is quicker once templates exist, and a shared multi-team platform is delivered in stages you review every one to two weeks.

Cost depends on scope, and the main question is whether you need one model's pipeline or a platform shared by several teams. Other drivers are the number of models and environments, how much training code must be refactored and your security requirements. After a discovery call we provide a written proposal with a fixed quote for the agreed scope, plus an estimate of ongoing GPU, storage and monitoring costs, with idle training resources and oversized endpoints designed out from the start.

Training data snapshots and model artefacts stay in your cloud account and chosen region, readable only by the pipelines and named people that need them. Each artefact in the registry is stored with a checksum, so the file deployed is provably the one that passed evaluation. Pipeline logs are kept free of raw personal data, secrets live in a managed vault, and every train, approve and deploy action is recorded. We work within your existing security and compliance requirements.

Yes, and the platform is designed with that in mind. Pipelines, infrastructure code, dashboards and runbooks live in your repositories and cloud accounts, and we document how to train, release, monitor and roll back each model. Your engineers and data scientists learn by running real releases with us during the project. Afterwards you can operate it alone or ask us for ongoing support, upgrades and model onboarding.