Alluxio Monthly Webinar: Optimize, Don't Overspend: Data Caching Strategy for AI Workloads
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Optimize, Don't Overspend: Data Caching Strategy for AI Workloads
As machine learning and deep learning models grow in complexity, AI platform engineers and ML engineers face significant challenges with slow data loading and GPU utilization, often leading to costly investments in high-performance computing (HPC) storage. However, this approach can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.
A better approach is adding a data caching layer between compute and storage, like Alluxio, which offers a cost-effective alternative through its innovative data caching strategy. In this webinar, Jingwen will explore how Alluxio's caching solutions optimize AI workloads for performance, user experience and cost-effectiveness.
What you will learn:
The I/O bottlenecks that slow down data loading in model training
How Alluxio's data caching strategy optimizes I/O performance for training and GPU utilization and significantly reduces cloud API costs
The architecture and key capabilities of Alluxio
Using Rapid Alluxio Deployer to install Alluxio and run benchmarks in AWS in just 30 minutes
Speaker: Jingwen Ouyang
Jingwen Ouyang is a Senior Product Manager at Alluxio with over 10 years of diverse data experience. Previously, she has worked as a Data Engineer at Meta and SanDisk. Jingwen received her BS and MS of EECS from MIT. She’s also a proud mom of her 2-year-old border collie, a certified snowboard instructor, and has a strong passion for basketball.
About Alluxio
Alluxio solves data challenges by providing a platform between your existing compute engines and storage systems, optimizing data access at every step of your data pipeline to accelerate analytics and AI workloads, on-prem, in the cloud, or both.
If you're interested in learning more and discovering how Alluxio can fit into your existing tech stack, schedule a demo here.
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