Blog
If you’re building large-scale AI, you’re already multi-cloud by choice (to avoid lock-in) or by necessity (to access scarce GPU capacity). Teams frequently chase capacity bursts, “we need 1,000 GPUs for eight weeks,” across whichever regions or providers can deliver. What slows you down isn’t GPUs, it’s data. Simply accessing the data needed to train, deploy, and serve AI models at the speed and scale required – wherever AI workloads and GPUs are deployed – is in fact not simple at all. In this article, learn how Alluxio brings Simplicity, Speed, and Scale to Multi-GPU Cloud deployments.
Alluxio's strong Q2 featured Enterprise AI 3.7 launch with sub-millisecond latency (45× faster than S3 Standard), 50%+ customer growth including Salesforce and Geely, and MLPerf Storage v2.0 results showing 99%+ GPU utilization, positioning the company as a leader in maximizing AI infrastructure ROI.
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This blog was originally published on the website of NetApp: https://www.netapp.com/blog/modernize-analytics-workloads-netapp-alluxio/
Imagine as an IT leader having the flexibility to choose any services that are available in public cloud and on premises. And imagine being able to scale your storage for your data lakes with control over data locality and protection for your organization. With these goals in mind, NetApp and Alluxio are joining forces to help our customers adapt to new requirements for modernizing data architecture with low-touch operations for analytics, machine learning, and artificial intelligence workflows.
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In the previous blog, we introduced Uber’s Presto use cases and how we collaborated to implement Alluxio local cache to overcome different challenges in accelerating Presto queries. The second part discusses the improvements to the local cache metadata.
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This article shares how Uber and Alluxio collaborated to design and implement Presto local cache to reduce HDFS latency.
This article introduces the design and implementation of metadata storage in Alluxio Master, either on heap and off heap (based on RocksDB).
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Raft is an algorithm for state machine replication as a way to ensure high availability (HA) and fault tolerance. This blog shares how Alluxio has moved to a Zookeeper-less, built-in Raft-based journal system as a HA implementation.
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With machine learning (ML) and artificial intelligence (AI) applications becoming more business-critical, organizations are in the race to advance their AI/ML capabilities. To realize the full potential of AI/ML, having the right underlying machine learning platform is a prerequisite.
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This article will discuss a new solution to orchestrating data for end-to-end machine learning pipelines that addresses the above questions. I will outline common challenges and pitfalls, followed by proposing a new technique, data orchestration, to optimize the data pipeline for machine learning.
Today, we are excited to announce the launch of Non-fungible token (NFT) as a new feature in our leading data orchestration platform.
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With the collaboration between Meta (Facebook), Princeton University, and Alluxio, we have developed "Shadow Cache" – a lightweight Alluxio component to track the working set size and infinite cache hit ratio. Shadow cache can keep track of the working set size over the past window dynamically and is implemented by a series of bloom filters. Shadow cache is deployed in Meta (Facebook) Presto and is being leveraged to understand the system bottleneck and help with routing design decisions.
This blog shares the practice of using Alluxio and Spark to accelerate the auto data tagging system in WeRide, an autonomous driving technology company.
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