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Most ML models never reach production. The ones that do degrade silently. MLOps is the engineering discipline that closes both gaps.
Seamless integration and maximum ROI for your machine learning models in production.
BNS builds the MLOps foundations — feature platforms, training pipelines, evaluation harnesses, drift monitoring, and model governance — that make ML reliable in production.
BNS MLOps programs build the infrastructure that makes machine learning reliable in production — feature stores, training pipelines, model registries, evaluation harnesses, drift detection, and the governance that satisfies model risk. The failure mode of enterprise ML is not bad models — it is the absence of the engineering infrastructure that keeps good models good. We build that infrastructure on the data and cloud platforms your engineering organisation already runs, and connect it to the data platform foundations that govern the features it depends on.
Our MLOps engineers have stood up production ML pipelines at enterprise scale — and have been through the drift incidents, retraining failures, and model-risk reviews that happen when the infrastructure is not there from day one. Evaluation harnesses are owned by a team, run on every merge, and carry SLAs that match your release cadence. Every model that goes to production has a drift budget, a retraining trigger, and a retirement criterion — agreed with the model-risk panel before training starts.
ML maturity audit, current pipeline state, model risk requirements, and platform readiness.
Feature store, training pipelines, model registry, and evaluation harness built on your existing stack.
Drift monitoring, compliance controls, and model risk integration established before first production model.
Platform team enablement, second model onboarded, and self-service model deployment achieved.
Online and offline feature stores with lineage, versioning, and point-in-time correctness.
Reproducible, scheduled, and trigger-based training pipelines on Kubeflow, Vertex AI, or SageMaker.
Centralised model registry with experiment tracking, artefact lineage, and approval workflows.
Automated evaluation suites run on every merge — with SLAs, regression detection, and quality gates.
Statistical drift detection, data quality alerts, and automated retraining trigger pipelines.
Model cards, audit trails, explainability frameworks, and model-risk committee integration.
BNS is technology-neutral by policy — our engineers work across the open-source and commercial stacks that enterprise platforms actually run on.
Automated training pipelines, model registries, and approval workflows cut the path from notebook to production from months to days. Model deployment frequency increases 6× on average after MLOps platform rollout — because the infrastructure does the work that was previously done manually.
Statistical drift detection, data quality monitoring, and automated retraining triggers keep production models within their accuracy guarantees as the underlying data evolves. Model incident rate drops 41% after MLOps platform rollout, because drift is caught before it compounds.
Evaluation harnesses are production infrastructure — owned by a team, run on every merge, and gated on SLAs. Model regressions are caught before they reach production, not discovered in a post-mortem. The same rigour that software teams apply to code quality is applied to model quality.
Model cards, audit trails, lineage documentation, and approval workflows are built into the platform — not assembled before a regulatory review. Every model that leaves the registry has a documented evaluation result, a risk assessment, and a signed-off deployment artefact.
A shared feature store with point-in-time correctness, lineage tracking, and online/offline serving eliminates the feature duplication and training-serving skew that corrupts model accuracy. Teams share features, reproduce experiments, and debug production behaviour from a single source of truth.
Our MLOps leads have run production ML platforms through real drift incidents, retraining failures, and model risk reviews. The platform designs reflect what breaks at scale, not what looks clean on an architecture diagram.
Kubeflow, Vertex AI, SageMaker, Databricks — we work across the MLOps stack and recommend what fits your platform and operating model. The strong opinions are about evaluation rigour, drift budgets, and governance discipline — not vendor selection.
BNS MLOps programs integrate with the data governance, lineage, and access control frameworks the data platform team runs — so model inputs are as governed as model outputs. The end-to-end audit trail covers features, training runs, evaluation results, and production deployments.
Training data, model artefacts, evaluation results, and drift monitoring data are handled under ISO 27001 and SOC 2 Type II controls. PII in training data, GDPR automated-decision obligations, and data residency requirements are handled at the pipeline level.
Measurable outcomes from the platforms our engineers shipped this year.
Most enterprise AI projects stall because the platform was never built to run models in production. A pragmatic blueprint for the foundation layer.
The hardest part of shipping AI in healthcare or financial services is not the model. It is proving that you did the uninteresting things right.
Boards over-index on burn-rate and under-index on delivery signal. The five charts that change the room.
Yes — we build on, extend, or replace your current MLOps tooling depending on what the maturity assessment recommends.
A foundational platform with training pipelines, model registry, and evaluation harness typically takes eight to twelve weeks.
Trusted data platforms and production AI that moves the P&L — not the lab.
Tailored data-driven solutions that optimise performance and drive measurable business outcomes.
End-to-end data platforms and pipelines for every stage of your data journey.
Share your brief and we’ll assemble two senior mlops practitioners for a 90-minute working session — no deck, no sales.