TL;DR: JFrog’s ML Model Management capabilities, which help bridge the gap between AI/ML model development and DevSecOps, are now Generally Available and come with a new approach to versioning models that benefit Data Scientists and DevOps Engineers alike. Model versioning can be a frustrating process with many considerations when taking models from Data Science to Production.
There are various ways to measure any given machine learning (ML) model’s ability to produce correct predictions, depending on the task that the system performs. Named Entity Recognition (NER) is one such task, in which a model identifies spans of sensitive data within a document. Nightfall uses NER models extensively to detect sensitive data across cloud apps like Slack, Microsoft Teams, GitHub, Jira, ChatGPT, and more.
New applications emerge at an almost impossible to keep-up-with pace, creating a constant challenge and blind spot for IT and security teams in the form of Shadow IT. Organizations must keep up by using tools that are automatically updated with latest developments and changes in the applications landscape to maintain proper security. An integral part of any SASE product is its ability to accurately categorize and map user traffic to the actual application being used.
Every year, JFrog brings the DevOps community and some of the world’s leading corporations together for the annual swampUP conference, aimed at providing real solutions to developers and development teams in practical ways to prepare us all for what’s coming next.
Machine learning (ML) is a subset of Artificial Intelligence (AI), which enables machines and software to automatically learn from historical data to generate accurate output without being programmed to do so. Many leading organizations today have incorporated machine learning into their daily processes for business intelligence. But the ability of machine learning can be altered by threat actors to be malicious, causing systems to malfunction, or to execute an attack.
To protect the business, security teams need to be able to detect and respond to threats fast. The problem is the average organization generates massive amounts of data every day. Information floods into the Security Operations Center (SOC) from network tools, security tools, cloud services, threat intelligence feeds, and other sources. Reviewing and analyzing all this data in a reasonable amount of time has become a task that is well beyond the scope of human efforts.
The power of the CrowdStrike Falcon® platform lies in its ability to detect and protect customers from new and unknown threats by leveraging the power of the cloud and expertly built machine learning (ML) models. In real-world conditions and in independent third-party evaluations, Falcon’s on-sensor and cloud ML capabilities consistently achieve excellent results across Windows, Linux and macOS platforms.
