GPU-Accelerated ML Platform on Kubernetes
Confidential Enterprise
Context
Data science teams required on-demand GPU resources for model development and serving, but existing infrastructure could not provide the flexibility or scale needed. Model training cycles were long and resource-inefficient.
Challenge
Design and deliver a GPU-accelerated machine learning platform on Kubernetes that provides on-demand GPU workloads, integrated ML development environments, and production serving capabilities across AWS and GCP.
Approach
Designed GPU-based ML platform on Kubernetes with TensorFlow, Kubeflow, and JupyterHub for interactive development workspaces. Implemented on-demand GPU provisioning to improve efficiency and speed for model development and serving. Built ML-Ops pipelines spanning AWS and GCP using Kubeflow, Google BigQuery, Google AI Platform, and AutoML for automated model training and evaluation.
Delivery
Phased delivery: platform architecture and GPU integration (4 weeks), ML development workspace automation (4 weeks), production serving pipeline with Kubeflow (4 weeks), team enablement and documentation (2 weeks).
Outcomes
On-demand GPU workloads
Data scientists access GPU resources instantly without infrastructure tickets or waiting
ML development velocity
Automated workspaces with TensorFlow, JupyterHub, and integrated experiment tracking
Cross-cloud ML pipelines
Production ML-Ops spanning AWS and GCP with automated training, evaluation, and serving
Legacy & Sustainability
Reusable ML platform blueprints, GPU scheduling patterns, and cross-cloud pipeline templates.
Stack
Timeline
14 weeks
What's Next
Expanding to additional model types and business units. Advanced monitoring and A/B testing capabilities in development.
Client identity is confidential. Detailed references and outcomes available under NDA.
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