On Wednesday, December 14th, 2022, Google announced an update to its AI-based spam-prevention system called SpamBrain. This update will use SpamBrain to detect and neutralize the impact of unnatural links on search results. The rollout of the update is expected to take two weeks and will affect all languages, potentially causing changes in ranking as any credit from these unnatural links is lost.
Google has always emphasized that links obtained primarily for artificial manipulation of Search rankings are considered link spam. With this update, the algorithms and manual actions in place are designed to nullify these unnatural links at scale. If users come across sites engaging in inorganic link building, they can report them to Google directly. For more information or specific feedback about this update, users can post a message in Google's help community.
In this post, Duy Nguyen, Ildar Akhmedyanov, Jacob N Scott and Karthikgeyan Elangovan discuss how to scale machine learning operations with high-performance computing (HPC) systems. The authors point out that as the amount of data and number of models used in machine learning increase, it becomes necessary to use HPC systems to take advantage of larger compute resources. They describe a typical HPC setup and discuss how it enables distributed training.
The authors also offer tips on optimizing data pipelines for better performance. One key step is ensuring that data is pre-processed before being sent to the compute nodes; this reduces network overhead and allows the nodes to run at maximum efficiency. Additionally, they discuss the importance of using frameworks such as TensorFlow or PyTorch for distributed training since these provide optimized methods for model parallelization.
Finally, the authors provide an overview of popular HPC systems such as Apache Spark and Kubernetes, which can be used for managing large scale machine learning jobs. They conclude by stating that HPC systems are essential for scaling machine learning operations in today's world of big data and complex models.