Machine Learning

AI Chihuahua: Why Machine Learning is Dogged by Failure and Delays - Ian Hellström (D2iQ)

AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.

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.