From experiment tracking to production model serving — our MLOps engineers bridge the gap between data science and reliable engineering, so your models reach production and stay there.
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From data ingestion to production model monitoring — we cover every layer of the modern ML engineering stack.
Design and automate end-to-end ML workflows — from data ingestion and feature engineering through model training, evaluation, and registration — with reproducible, version-controlled pipelines that run on every code change.
Establish a centralised model registry with full lineage tracking — storing model artefacts, hyperparameters, training metrics, and dataset versions so every production model is auditable and reproducible.
Deploy models as scalable, low-latency inference endpoints using REST or gRPC — with A/B testing, shadow mode deployment, canary rollouts, and automatic scaling to handle variable inference traffic.
Monitor production models for data drift, concept drift, prediction distribution shifts, and performance degradation — with automated alerting and retraining triggers when model quality falls below defined thresholds.
Design and implement feature stores that eliminate training-serving skew, enable feature reuse across teams, and provide consistent, low-latency feature retrieval for both batch training and online inference.
Build or consolidate your internal ML platform — standardising experiment environments, compute provisioning, GPU scheduling, and developer tooling so data scientists focus on modelling, not infrastructure.
Our engineers hold GCP Professional ML Engineer, AWS ML Specialty, Databricks ML certifications alongside Kubernetes CKA — combining ML infrastructure depth with production engineering rigour.
We speak both languages fluently — translating data science notebook experiments into production-grade, monitored, and maintainable ML systems that engineering teams can confidently operate.
Whether your team uses Vertex AI, SageMaker, Azure ML, or a self-hosted Kubeflow stack — we adapt to your platform rather than forcing migration to a new one.
We treat model endpoints like production services — with SLOs, alerting, on-call runbooks, canary deployments, and automated rollback — so a degraded model never silently serves bad predictions.
Whether you need ML pipeline automation, a feature store, model serving infrastructure, or 24/7 production model monitoring — our MLOps engineers are ready.