How Can AI Platforms Adapt to Hybrid or Multi-Cloud Environments?

This article was originally published on Spiceworks. https://www.spiceworks.com/tech/artificial-intelligence/guest-article/adapting-ai-platform-to-hybrid-cloud/ This blog discusses the challenges of implementing AI platforms in hybrid and multi-cloud environments and shares examples of organizations that have prioritized security and optimized cost management using the data access layer. In recent years, AI platforms have undergone significant transformations as GenAI and AI continue to … Continued

Maximize GPU Utilization for Model Training

GPU utilization or GPU usage, is the percentage of GPUs’ processing power being used at a particular time. As GPUs are expensive resources, optimizing their utilization and reducing idle time is essential for enterprise AI infrastructure. This blog explores bottlenecks hindering GPU utilization during model training and provides solutions to maximize GPU utilization. 1. Why … Continued

Accelerating Data Loading in Large-Scale ML Training With Ray and Alluxio

In the rapidly-evolving field of artificial intelligence (AI) and machine learning (ML), the efficient handling of large datasets during training is becoming more and more pivotal. Ray has emerged as a key player, enabling large-scale dataset training through effective data streaming. By breaking down large datasets into manageable chunks and dividing training jobs into smaller … Continued

The Data-Driven Heartbeat of Artificial Intelligence

This article was initially posted on Solutions Review. Artificial Intelligence (AI) has consistently been in the limelight as the precursor of the next technological era. Its limitless applications, ranging from simple chatbots to intricate neural networks capable of deep learning, promise a future where machines understand and replicate complex human processes. Yet, at the heart of … Continued

Building High-performance Data Access Layer for Model Training and Model Serving for LLM

Bringing a large language model from its initial training to deployment requires numerous systems and components. At Zhihu, we grappled with a multi-cloud, cross-region AI platform, requiring an efficient solution to facilitate the rapid training and delivery of models for production use cases. This led us to adopt Alluxio, the high-performance data access layer for … Continued

Alipay: Optimizing Alluxio for Efficient Large-Scale Training on Billions of Files

Chuanying Chen, Senior Software Engineer at Ant Group, provides a deep dive into the practices of optimizing Alluxio for reliable, scalable, and high-performance large-scale training on billions of files. 1. Background Ant Group, formerly known as Ant Financial, is an affiliate company of the Chinese conglomerate Alibaba Group. The group owns the world’s largest mobile … Continued

What’s Next for Data Analytics, AI, and Cloud in 2023?

Originally published on vmblog.com: https://vmblog.com/archive/2022/12/27/alluxio-2023-predictions-what-s-next-for-data-analytics-ai-and-cloud-in-2023.aspx As we enter 2023, the world of analytics, AI, and cloud is entering an exciting new phase, with a wide range of innovations and developments set to reshape the landscape. Below are some trends that will have the most impact in the coming year. Trend 1: Cloud cost optimization is … Continued

Unified Data API for Distributed Cloud Analytics and AI

This presentation focuses on how Alluxio helps the big data analytics stack to be cloud-native. The trending Cloud object storage systems provide more cost-effective and scalable storage solutions but also different semantics and performance implications compared to HDFS. Applications like Spark or Presto will not benefit from the node-level locality or cross-job caching when retrieving data from the cloud object storage. Deploying Alluxio to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly.

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