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Confidential Client
Infrastructure-Agnostic Enterprise ML Platform — 3 Generations
A global technology consultancy needed a reusable, production-grade ML platform that could be deployed across multiple client environments without being tied to a specific cloud vendor.
Business Challenge
The client needed an ML platform that could be adopted across multiple enterprise client engagements without rearchitecting for each. The platform had to cover the full ML lifecycle, be cloud-agnostic, and maintain enterprise-grade monitoring, CI/CD, and governance across all deployments.
Key Features
Infrastructure-agnostic, Spark-centric ETL with full CI/CD across dev, UAT, and production.
ML pipeline orchestration with Argo Workflows and Kubeflow for experiment and run management.
TensorFlow/Keras model serving via Seldon with canary deployment support.
Grafana, Prometheus, and GrayLog for end-to-end model performance and infrastructure monitoring.
mlflow for experiment tracking, model versioning, and registry management.
Docker and Kubernetes as the core deployment substrate enabling cloud-agnostic portability.
Three major platform generations delivered as the ML ecosystem matured.
Results
Reusable ML platform adopted across multiple enterprise client engagements.
3 major platform versions delivered across a 2-year engagement.
Deployment portability across AWS, GCP, and on-premise environments achieved.
Model serving latency and throughput monitored in production via Seldon + Prometheus.
Tech Stack
Kubeflow
TensorFlow
Seldon
Grafana