Machine Learning & MLOps

From notebook to production pipeline.

Feature stores, model registries, CI/CD for ML, and drift monitoring. We build the infrastructure that keeps models sharp and your team moving fast.

See our work

SageMaker certified

Azure ML partner

Kubeflow expertise

Capabilities

What we build

MLOps is infrastructure. We build it once, correctly, so your team never has to debug a training pipeline at 2am again.

Feature Stores

Online and offline feature serving with point-in-time correctness. Eliminates training-serving skew and makes features reusable across teams.

FeastTectonSageMaker Feature Store

Model Registry & Versioning

Centralised model artefact management, lineage tracking, stage transitions, and approval workflows. Know exactly what's running in production and why.

MLflowW&BSageMaker Model Registry

CI/CD for ML

Automated training pipelines triggered by data or code changes. Model evaluation gates, canary deployments, and rollback on metric degradation.

KubeflowSageMaker PipelinesGitHub Actions

Drift Monitoring

Data drift, concept drift, and prediction quality monitoring in production. Automated alerts before model degradation reaches users.

EvidentlyWhylogsArize

Inference Infrastructure

Low-latency serving with autoscaling, batching, and multi-model endpoints. GPU right-sizing and quantisation to keep inference costs sane.

TritonTorchServeSageMaker Endpoints

Experiment Management

Reproducible training runs with hyperparameter tracking, dataset versioning, and collaborative experiment review. Science you can audit.

Weights & BiasesMLflowComet

How we engage

Structured delivery, start to finish.

Model audit

We assess existing model quality, training infrastructure, and deployment gaps. Output: a clear picture of what's blocking production confidence.

Feature & data pipeline

End-to-end data pipelines with point-in-time correctness, lineage, and feature reuse. The foundation that prevents training-serving skew.

Training pipeline

Reproducible, automated training triggered by code or data changes. Evaluation gates, metric thresholds, and automated retraining schedules.

Production & monitoring

Serving infrastructure with autoscaling, A/B testing, and drift alerts. Your model stays sharp without manual intervention.

Technologies

The stack we deploy on.

PyTorchTraining

TensorFlowTraining

scikit-learnClassical ML

XGBoostGradient boosting

KubeflowPipelines

MLflowTracking

Weights & BiasesExperiments

FeastFeature store

EvidentlyMonitoring

SageMakerPlatform

Azure MLPlatform

TritonServing

Outcomes

Production results.

6 wk

Notebook to production

average for ML pipeline build

40%

Inference cost reduction

through quantisation & batching

99.4%

Pipeline reliability

automated training runs

Faster experimentation

vs pre-MLOps baseline

FAQ

Common questions.

Don't see yours? Ask us directly — we usually reply within a working day.

When you have more than one model in production, or when model retraining is manual and error-prone. Below that threshold, simpler tooling is often better.

Both. Managed platforms reduce operational burden for most teams. Kubernetes gives more control for high-volume serving or when platform costs become prohibitive. We make the call based on your team's operational capacity.

Data validation, schema enforcement, and automated quality checks are built into the pipeline before training starts. We use Great Expectations or custom validators depending on your data stack.

Yes — our preferred mode. We embed alongside your data scientists, build the infrastructure layer, and leave your team owning it. We train through pairing, not documentation dumps.

◐ Currently booking — Q3 2026

Ready to take ML seriously in production?

Book a call. We'll review your current model lifecycle and tell you exactly where the gaps are.

hello@cybergenesisx.com