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Confidential Client
Infrastructure-Agnostic Enterprise ML Platform — 3 Generations
Platform generations
3
Engagement length
2 years
Deployment targets
AWS · GCP · on-prem
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.
What changed
Before
Per client
Bespoke ML setup
Portability
None
rebuilt each time
After
Pipelines
Kubeflow
TensorFlow
Serving
Seldon
Monitoring
Prometheus
Grafana
Targets
AWS
GCP
On-premise
One platform definition that lands unchanged on three very different substrates — which is what made it reusable across clients.
What we built
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.
Stack
Engagement: 2 years · Real-time & Industrial Data Platforms, Product Engineering, Platform & Cloud Engineering, AI Infrastructure & MLOps