Abnormal Blog
Dan Shiebler
Head of Machine Learning
Dan is the Head of Machine Learning at Abnormal Security, where he builds systems to catch cyberattacks. Before Abnormal, Dan worked at Twitter, first as a staff machine learning engineer in Cortex, and later as the manager of the web ads machine learning team. Before Twitter, Dan worked as a senior data scientist at Truemotion, where he developed smartphone sensor algorithms to price car insurance.
Discover how the Abnormal attack detection team utilizes feature systems, advanced language models, and per-customer understanding in our approach to machine learning in cybersecurity.
We are excited to share that Abnormal has recently deployed a BERT Large Language Model (LLM), pretrained from Google on a large corpus of data, and applied it to stop advanced attacks.
In episode 9 of Abnormal Engineering Stories, Dan sits down with Mukund Narasimhan to discuss his perspective on productionizing machine learning.
At Abnormal, we pride ourselves on our excellent machine learning engineering team. Here are some patterns we use to distinguish between effective and ineffective ML engineers.
Here at Abnormal, our machine learning models help us spot trends and abnormalities in customer data in order to catch and prevent cyberattacks.