Machine Learning


Machine learning log analysis and why you need it

Your log analysis solution works through millions of lines of logs, which makes implementing a machine learning solution essential. Organizations are turning to machine learning log alerts as a replacement or enhancement of their traditional threshold alerts. As service uptime becomes a key differentiator, threshold alerts are only as good as your ability to foresee an issue.


Cybersecurity Experts Discuss: Machine Learning for Security Applications

In a discussion between Ben Harrison, Director SOC and Security Services at Cygilant and Jake McCabe, CISSP, Presales Director at LogPoint, we summarize why machine learning and a SOC go hand in hand. Traditional SIEMs offer a rules-based approach as it looks for alerts. Because you can easily write a search, it’s very good at picking out known-bad entities. However, there are certain things that can occur which are not so black and white.


How GPUaaS On Kubeflow Can Boost Your Productivity

Tapping into more compute power is the next frontier of data science. Data scientists need it to complete increasingly complex machine learning (ML) and deep learning (DL) tasks without it taking forever. Otherwise, faced with a long wait for compute jobs to finish, data scientists give in to the temptation to test smaller datasets or run fewer iterations in order to produce results more quickly.


AI, ML and ROI - Why your balance sheet cares about your technology choices

Much has been written on the growth of machine learning and its impact on almost every industry. As businesses continue to evolve and digitally transform, it’s become an imperative for businesses to include AI and ML in their strategic plans in order to remain competitive. In Competing in the Age of AI, Harvard professors Marco Iansiti and Karim R. Lakhani illustrate how this can be confounding for CEOs, especially in the face of AI-powered competition.


What Are Feature Stores and Why Are They Critical for Scaling Data Science?

A feature store provides a single pane of glass for sharing all available features across the organization. When a data scientist starts a new project, he or she can go to this catalog and easily find the features they are looking for. But a feature store is not only a data layer; it is also a data transformation service enabling users to manipulate raw data and store it as features ready to be used by any machine learning model.