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AI/ML Infra Meetup with OpenAI, Poshmark, & Alluxio Recap
Poshmark recordings and slides are available now!
No more waiting. The Poshmark sessions are now available on demand! Don’t miss these informative sessions from the AI/ML Infra Meetup with OpenAI, Poshmark, and Alluxio – Watch on-demand now!
In “Scaling Vector Databases for E-Commerce Visual Search: Architectural Strategies for 150M+ Product Search & Recommendations,” Mahesh Pasupuleti, VP of DS, ML & Data Infra @ Poshmark, discusses how Poshmark developed Posh Lens, an advanced visual search engine that revolutionizes how shoppers discover and purchase items.
Koundinya Pidaparthi, VP of Analytics, presents on “Scaling Experimentation Platform in Digital Marketplaces: Architecture, Implementation & Lessons Learned,” where he will delve into the best practices, implementation, and learnings from building a scalable experimentation platform at Poshmark.
Efficient Data Access Strategies for Large-scale AI
Architecture and Considerations in Machine learning Pipeline – Check out one of our most popular white papers 👏
Download it now and delve into the latest insights about modern AI architecture to have a better understanding of the challenges of feeding data-hungry GPUs in the cloud. This whitepaper also provides you with strategies on how to level up your AI platform with scalability, mobility and faster data access and real-world examples from leading AI teams at Fintech and Internet Companies.
We’re Hiring! New Positions Open
Join us to build the foundation for the future of data and AI!
We currently have 30+ opportunities across the globe! Learn more about our job openings in Solution Engineering and Engineering. Are you awesome or know of anyone to refer? Check out the full list of opportunities and apply here.
Upcoming Events
Alluxio’s Bin Fan and Hope Wang will present on stage at Community Over Code NA 2024 this Wednesday, Oct 9. In this presentation, Hope and Bin will analyze these challenges through a case study on Uber’s large deployment analytics SQL platform on HDFS and GCS. They will share their findings of unexpected cost implications with standard I/O optimizations like table scans, filters, and broadcast joins when implemented in cloud environments. Furthermore, they will highlight the need to optimize I/O for AI/ML training for performance, GPU utilization, and cost efficiency.
Join Tom Luckenbach, Alluxio Solutions Engineering Manager, to learn how Alluxio enables high-speed, cost-effective data access for AI training workloads in hybrid and multi-cloud architectures, while eliminating the need to manage data copies across regions and clouds.
What Tom will share:
- AI data access challenges in cross-region, cross-cloud architectures.
- The architecture and integration of Alluxio with frameworks like PyTorch, TensorFlow, and Ray using POSIX, REST, or Python APIs across AWS, GCP and Azure.
- A live demo of an AI training workload accessing cross-cloud datasets leveraging Alluxio’s distributed cache, unified namespace, and policy-driven data management.
- MLPerf and FIO benchmark results and cost-savings analysis.
Open Source Summit Japan 2024 | Tue Oct 29 12:00pm JST | Tokyo, Japan
Alluxio will be at OSS Japan 2024 this October! Join Bin Fan and Hope Wang for their session on Tuesday, October 29 at 12:00 pm JST where Bin and Hope will highlight the need for a paradigm shift in optimizing data-intensive applications for the cloud and advocate for developing new I/O strategies, balancing performance and costs while tailored to cloud ecosystems’ unique demands.
Past Events On-demand
Alluxio Webinar | Optimize, Don’t Overspend: Data Caching Strategy for AI Workloads
Are you making costly investments in high-performance computing storage trying to address the challenges with slow data loading and GPU utilization? This approach may not be optimal, which can result in overspending without addressing the core issues of data bottlenecks and infrastructure complexity.
In our latest webinar, Senior Product Manager, Jingwen Ouyang, introduced a better approach: adding a data caching layer between compute and storage, like Alluxio, to optimize AI workloads for performance, user experience, and cost-effectiveness.
Got a tech question for the Alluxio Community? Chat with us on Slack!
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If you like our product, please give it a star on GitHub, and share the goodness!
WHITEPAPERs
Rise of the Data Access Layer for Analytics & AI
Choosing the Right Architecture for Enterprise AI Workloads in Production
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