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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.
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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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Alluxio is the data orchestration platform to unify data silos across heterogeneous environments. This is the last article in a series to give you the basics of Alluxio’s architecture and solution.
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Running Presto with Alluxio is gaining popularity in the community. It avoids long latency reading data from remote storage by utilizing SSD or memory to cache hot dataset close to Presto workers. Presto supports hash-based soft affinity scheduling to enforce that only one or two copies of the same data are cached in the entire cluster, which improves cache efficiency by allowing more hot data cached locally. The current hashing algorithm used, however, does not work well when cluster size changes. This article introduces a new hashing algorithm for soft affinity scheduling, consistent hashing, to address this problem.
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As data stewards and security teams provide broader access to their organization’s data lake environments, having a centralized way to manage fine-grained access policies becomes increasingly important. Alluxio can use Apache Ranger’s centralized access policies in two ways: 1) directly controlling access to virtual paths in the Alluxio virtual file system or 2) enforcing existing access policies for the HDFS under stores.
To provide model training with the best experience, Tencent has implemented a 1000-node Alluxio cluster and designed a scalable, robust, and performant architecture to speed up Ceph storage for game AI training. This blog will give you insight into how Alluxio has been implemented and optimized at Tencent.
2021 marked accelerated growth for the Alluxio Open Source Project. We could not be more grateful for what the community has achieved together in this past year. This blog provides a glimpse of the year long summary of our community growth.
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This blog is the last one in the machine learning series. Our first blog introduced the what and why of our solution, and the second blog compared traditional and Alluxio solutions. This blog will demonstrate how to set up and benchmark the end-to-end performance of the training process.