Abraham Starosta

Abraham Starosta is an applied scientist at Splunk, where he works on streaming machine learning and Natural Language Processing problems. Prior to Splunk, Abraham was an NLP engineer at high growth technology startups like Primer and Livongo, and interned at Splunk in 2014. He completed his B.S and M.S in Computer Science from Stanford, where his research focused on weak supervision and multitask learning.

Platform 11 Min Read

Prevent Data Downtime with Anomaly Detection

Learn how to use Machine Learning in Splunk to create an automatic alerting system for Admins that sends alerts whenever there is unexpected downtime or spike in ingestion volume.

Observability 10 Min Read

How Splunk Is Parsing Machine Logs With Machine Learning On NVIDIA’s Triton and Morpheus

A global workforce, combined with the growing need for data, is driving an increasingly distributed and complex attack surface that needs to be protected. Sophisticated cyberattacks can easily hide inside this data-centric world, making traditional perimeter-only security models obsolete. The complexity of this interconnected ecosystem now requires one to assume that the adversary is already within the network and consequently must be detected there, not just at the perimeter.