Production-grade MLOps pipelines for Malaysian enterprises and GLCs. From model training to real-time inference — governed, observable, and compliant by design._
End-to-end machine learning operations — from raw data to live production inference. Every stage instrumented, governed, and observable.
Automated pipelines that train, test, and deploy models on every commit. GitOps-driven workflows with DVC and MLflow experiment tracking.
Real-time drift detection, performance degradation alerts, and automated retraining triggers. Full observability on every prediction.
Production-grade Retrieval-Augmented Generation with Graph RAG for structured knowledge. Grounded, contextual, and always current.
Domain-specific LLM adaptation on proprietary datasets. LoRA, QLoRA, and full fine-tuning with automated benchmarking.
XAI, model cards, bias audits, and risk documentation. GDPR, AML, and KYC compliance baked into the deployment lifecycle.
Kubernetes-native model serving with auto-scaling, A/B traffic splitting, canary rollouts, and blue-green deployments.
Define your entire ML pipeline in YAML. Version control your experiments, features, and deployments the same way you manage application code.
Declare data sources, feature transforms, training configs, and evaluation thresholds in a single pipeline.yaml
Every run logs metrics, artifacts, and lineage. Compare experiments across teams and time.
One-command promotion with automated validation gates, rollback policies, and compliance checks.
Every model deployment generates audit trails, bias reports, and explainability documentation automatically.
From pilot to production in weeks, not months. HRDF-claimable implementation programs available for Malaysian enterprises and GLCs.
No commitment. Includes free pipeline audit and architecture review.