As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.
In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.
As the AI landscape rapidly evolves, the advancements in generative AI technologies, such as ChatGPT, are driving a need for robust data infrastructures tailored for large language model (LLM) training and inference in the cloud. To effectively leverage the breakthroughs in LLM, organizations must ensure low latency, high concurrency, and scalability in production environments.
In this Alluxio-hosted webinar, Shouwei presented on the design and implementation of a distributed caching system that addresses the I/O challenges of LLM training and inference. He explored the unique requirements of data access patterns and offer practical best practices for optimizing the data pipeline through distributed caching in the cloud. The session featured insights from real-world examples, such as Microsoft, Tencent, and Zhihu, as well as from the open-source community. Watch this recording to get a deeper understanding of how to harness scalable, efficient, and robust data infrastructures for LLM training and inference.
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At Poshmark, we developed a homegrown experimentation platform, Lightspeed, that allowed us to make reliable and confident reads on product changes, which led to a 10x growth in experiment velocity and positive business outcomes along the way.
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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