Recommendations to Level Up Your Machine Learning Platform

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.

Orchestrating Data for Machine Learning Pipelines

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.

Improving Presto Architectural Decisions with Alluxio Shadow Cache at Meta (Facebook)

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.

Using Consistent Hashing in Presto to Improve Caching Data Locality in Dynamic Clusters

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.