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.
This blog is the second in the machine learning series following the previous one, which discussed Alluxio’s solution to improve training performance and simplify data management. With the help of Alluxio, loading data from cloud storage, training and caching data can be done in a transparent and distributed way as a part of the training process, thus improving training performance and simplifying data management. In this blog 2 of the series, we focus on comparing traditional solutions with Alluxio’s.
In this blog, we provide an overview of Alluxio’s AI/ML model training solution. For more details about the reference architecture and benchmarking results, please refer to the full length whitepaper.
Running inference at scale is challenging. In this blog, we will share our observations and the practice to use Alluxio to speed up the I/O performance for large-scale ML/DL offline inference at Microsoft Bing.
Metadata synchronization (sync) is a core feature in Alluxio that keeps files and directories consistent with their source of truth in under storage systems, thus making it simple for users to reason the data retrieved from Alluxio. Meanwhile, understanding the internal process is important in order to tune the performance. This article describes the design and the implementation in Alluxio to keep metadata synchronized.
With this release, Alluxio has strengthened its position as a de-facto data unification and acceleration solution in data analytics and machine learning pipelines. The solution is optimized to support Spark, Presto, Tensorflow, and PyTorch, and is available on multiple cloud platforms such as AWS, GCP, and Azure Cloud, and also on Kubernetes in private data centers or public clouds.
Alluxio is the data orchestration platform to unify data silos across heterogeneous environments. The following blog will discuss the architecture combining Spark with Alluxio.
Unisound is an artificial intelligence company focusing on Internet of Things services. Unisound’s AI technology stacks include the perception and expression capabilities of signals, voices, images, and texts, and the cognitive technologies such as knowledge, understanding, analysis, and decision-making, towards a multi-modal AI system. Atlas is the supercomputing platform supporting all kinds of AI applications including model training and reasoning inferencing.
This blog is the first in a series introducing Alluxio as the data platform to unify data silos across heterogeneous environments. The next blog will include insights from PrestoDB committer Beinan Wang to uncover the value for analytics use cases, specifically with PrestoDB as the compute engine.