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.
The Alluxio core engineering team re-designed things to come up with a more efficient and transparent way for users to leverage data orchestration through the POSIX interface. This enables much better performance for ML workloads where data is accessed via the POSIX interface.