Bridge the gap between ML experiments and production. I build automated pipelines, reliable model serving, and observable ML platforms that scale with your AI ambitions.
Complete ML lifecycle management — from notebook to production at scale
End-to-end automated training pipelines with data validation, model training, evaluation, and registry. Reproducible experiments every time.
Deploy models as scalable APIs with auto-scaling, blue-green deployments, and canary rollouts. REST, gRPC, and streaming inference endpoints.
Real-time monitoring for data drift, model degradation, and prediction quality. Automated retraining triggers and alerting dashboards.
MLflow and Weights & Biases integration. Track experiments, compare runs, manage model versions, and maintain full lineage from data to deployment.
Centralized feature engineering with Feast or Vertex AI Feature Store. Consistent features across training and serving, with point-in-time correctness.
Model cards, bias detection, explainability (SHAP/LIME), and audit trails. Compliance-ready ML systems with full reproducibility.
Deep experience across leading MLOps platforms
Pipelines, Model Registry, Feature Store, Endpoints, AutoML, TFX
Google CloudPipelines, Model Monitor, Feature Store, Endpoints, Ground Truth
AWSKubeflow Pipelines, KServe, Katib, Training Operator
Kubernetes-NativeTracking, Model Registry, Projects, Deployments
Open SourceFlexible engagements for every MLOps maturity level
Single model deployment
1–2 week delivery
Automated ML pipeline
4–6 week delivery
Full ML platform
8–12 week delivery
MLOps (Machine Learning Operations) applies DevOps principles to ML systems. It automates the lifecycle of ML models — from training to deployment to monitoring. Without MLOps, most ML projects fail to reach production or degrade silently after deployment.
It depends on your stack. If you're on GCP, Vertex AI is the natural choice. AWS shops benefit from SageMaker. For cloud-agnostic or on-prem setups, Kubeflow + MLflow is an excellent open-source combination. I'll help you choose based on your constraints.
I implement statistical drift detection (PSI, KS test, Jensen-Shannon divergence) on both input features and model predictions. When drift exceeds thresholds, automated alerts fire and retraining pipelines can be triggered — either automatically or with human approval.
Yes. I design MLOps platforms that meet data scientists where they are — Jupyter notebooks, experiment tracking, easy model deployment. I also provide training sessions and documentation so your team can self-serve after the initial setup.
Let's build an MLOps platform that turns your ML experiments into production value — reliably and repeatedly.