Hybrid Cloud AI for Zebra Technologies with Run:ai
Client
Zebra Technologies
Industry
Manufacturing & Technology
AI Tech Solution
Hybrid Cloud AI Infrastructure
Solution Provider
Run:ai
Challenge
Zebra Technologies, a global leader in manufacturing and enterprise solutions, needed to optimize AI model training across on-premises and cloud environments. The company develops AI/ML-powered computer vision models for industries such as retail, logistics, and healthcare, but struggled with resource allocation and scalability. Key challenges included: Inefficient GPU utilization, as AI workloads were not dynamically allocated between on-prem and cloud resources. Manual compute allocation, requiring static Excel spreadsheets for resource tracking. Hybrid cloud management complexities, where researchers had to use different tools for managing multiple GPU clusters. Zebra Technologies needed a unified AI workload orchestration solution to maximize on-prem GPU usage while seamlessly scaling AI workloads to the cloud when needed.
Solution
Zebra Technologies deployed Runa:ais hybrid AI workload orchestration platform, allowing AI teams to dynamically allocate compute resources across on-prem and cloud environments. With Runa:ais GPU scheduling and virtualization technology, Zebra was able to: Eliminate spreadsheet-based manual tracking, automating GPU resource allocation. Prioritize on-prem GPU usage first, only scaling to the cloud when additional compute resources were required. Centralize monitoring and management, providing a single control panel for tracking AI workloads across on-prem and cloud environments. Runa:ais automated hybrid AI infrastructure ensured that Zebra could run AI experiments efficiently, reducing manual intervention and compute waste.
Results
By implementing Runa:ais AI workload orchestration platform, Zebra Technologies achieved seamless hybrid AI training, optimizing resource utilization and cost efficiency. Full utilization of on-prem GPUs, ensuring that local resources were used efficiently before scaling to the cloud. Faster AI model training, reducing time-to-market for AI-powered products. Eliminated manual GPU tracking, as Run:ai automated resource allocation across hybrid AI infrastructure. With Runa:ais hybrid AI workload management solution, Zebra Technologies successfully streamlined AI model development, accelerated experimentation, and optimized AI resource efficiency. Case Study Highlights Seamless hybrid AI scaling: Run:ai enabled dynamic allocation of on-prem and cloud GPU resources for AI workloads. Optimized AI compute usage: AI model training was prioritized on on-prem GPUs, reducing cloud costs. Automated AI resource management: No more spreadsheets—Run:ai automated real-time GPU tracking and scaling. Suncoast Credit Union UI Path Case Study 2025-02-02 10_14_44.pdf https://www.uipath.com/resources/automation-case-studies/suncoast-credit-union-secures-member-trust-through-ai-based-fraud-prevention
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