MLOps — Model Deployment, Monitoring & Reliability

Our MLOps consulting services cover model deployment and serving infrastructure, ML pipeline automation using tools like Kubeflow, MLflow, and ZenML, model monitoring and drift detection, experiment tracking, feature store design, and GPU cost optimisation. Engagements are scoped to where your current pipeline breaks down, whether that is slow deployments, degrading accuracy, or runaway inference costs.

DevOps automates software delivery. MLOps applies those principles to machine learning, but adds the additional complexity of data versioning, model training pipelines, experiment tracking, accuracy monitoring, and retraining triggers. A model that worked last month may degrade as real-world data drifts from training data, which standard DevOps tooling does not detect.

We work with MLflow and Weights and Biases for experiment tracking and model registry, Kubeflow and ZenML for pipeline orchestration, Evidently and Arize for model monitoring and drift detection, BentoML and Triton for model serving, and standard CI/CD tooling (GitHub Actions, ArgoCD) for deployment automation. Tool selection is always based on your existing stack and scale requirements.

We instrument every deployed model with statistical drift monitoring across input data distributions and output quality metrics. When drift exceeds defined thresholds, the system triggers an alert and, where retraining pipelines are in place, initiates an automated retraining cycle. This keeps models accurate without requiring manual intervention from your engineering team.

We begin with a production readiness assessment: current deployment process, monitoring coverage, and cost profile. From there we design the target MLOps architecture, build or migrate your pipeline infrastructure, instrument monitoring and alerting, and hand over with documented runbooks. Most engagements run 8–14 weeks depending on the number of models in scope and the complexity of your existing infrastructure.