Scaling Machine Learning Workloads with Ray

Scaling Machine Learning Workloads with Ray
Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications and ML workloads, without requiring any distributed systems expertise, making distributed programming easy.
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OTHER ON-DEMAND WEBINARS

3 Benefits of Using Cohesity with Microsoft Azure

Cohesity

The benefits of hybrid cloud are well established. Now customers are looking for a solution that can combine the best of on-premises infrastructure with the public cloud for solving their data management challenges. A 2017 451 Research survey found that data capacity & growth, meeting DR requirements, and high cost of storage were the three biggest storage headaches for customers and public cloud can ease that burden.
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Eliminate Invoice Review Headaches with AI-enabled Automated Solutions

Join Aavenir to learn how the AI-driven Invoiceflow solution can help you overcome invoice review and processing headaches, and streamline vendor communication with collaboration portals.
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API monitoring and troubleshooting in a hybrid world

Anypoint Monitoring helps you to proactively identify and resolve issues — all in one place with real-time visibility into your APIs and integrations. Ensure business continuity and manage mission-critical deployment with real-time visibility into app performance, customizable dashboards, advanced alerts, and instant access to historical log data.
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Edge Ai summit dummy

For its 5th year, Kisaco Research’s Edge AI Summit returns to the Bay Area, CA, fully face-to-face and co-located with the industry leading AI Hardware Summit. This year’s event has been re-thought, re-designed and re-built from the ground up through research with industry experts to support enterprise adopters, OEMs, AI software, and hardware providers in their pursuit of deploying economical, efficient, and optimized AI at the edge.
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