Decrease time and effort to productionize AI Models using KubeRay

• Inefficient resource utilization – GPUs often underutilized.
• Slow experimentation cycles – Developers had to wait for availability.
• Complex deployments – Moving models from research to production was error-prone and time-consuming.

Our Approach

We introduced Ray on Kubernetes (KubeRay) as the foundation of their new AI platform. Our solution included:
1. Unified AI Platform on Kubernetes – Migrated workloads from siloed GPU servers to a scalable Kubernetes cluster, orchestrated with KubeRay.
2. Elastic Scaling for ML Workloads – Enabled auto-scaling of Ray clusters, ensuring efficient GPU and CPU usage across multiple teams.
3. End-to-End MLOps Integration – Integrated Ray with CI/CD pipelines, feature stores, and observability tools for seamless model lifecycle management.
4. Production-Ready Model Serving – Deployed models with Ray Serve, allowing the team to serve predictions at scale with minimal overhead.

Impact & Results

• 80% Faster Experimentation – Teams could launch parallel training jobs without waiting for hardware.

• 40% Cost Optimization – GPUs and CPUs dynamically scaled, eliminating idle infrastructure costs.

• Streamlined Deployment – Models moved from research to production in hours instead of weeks.

• Improved Collaboration – A shared, self-service platform empowered both data scientists and engineers to innovate faster.

Key Technologies Used

• Ray & KubeRay – Distributed AI/ML training and serving on Kubernetes

• Kubernetes (EKS/AKS/GKE) – Cloud-native orchestration for scalable workloads

• ML Pipelines – CI/CD for ML with GitOps integration

• Observability – Prometheus, Grafana, and Loki for monitoring and logging

Outcome:The client’s AI team transformed from struggling with fragmented GPU resources to running a fully cloud-native AI platform. With Ray on Kubernetes, they can now innovate faster, deploy models seamlessly, and scale AI workloads with confidence.