article thumbnail

Duo Security created open tools and techniques to identify large Twitter botnet

Security Affairs

“By applying a methodical data science approach to analyzing our dataset, we were able to build a classifier that effectively finds bots at a large scale.” Practical data science techniques can be used to create a classifier that could help researchers in finding automated Twitter accounts.

Security 189
article thumbnail

AI governance versus model management: What’s the difference?

Collibra

So when Data Science teams think about AI governance, they think model management. Model management, an element of MLOps, ensures that data science best practices are followed when building and maintaining an AI model throughout its lifecycle. Notice that “AI use case” is the subject, not the AI model.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

What Happens to Electronic Records in the Archives?

The Texas Record

What is metadata, and why is it so important when archiving electronic records? Metadata is descriptive information (data) about stuff. Without all of this metadata you would be holding a blank box that may or may not be free but definitely is a mystery. This is such a pervasive thing that it can be hard to describe.

Archiving 116
article thumbnail

AI Governance: Break open the black box

IBM Big Data Hub

This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. It drives a complete governance solution without the excessive costs of switching from your current data science platform. Lifecycle Governance: Monitor, catalog and govern AI models from anywhere and throughout the AI lifecycle.

article thumbnail

Harnessing Analytical Insights and Illuminating the Physical Realm of Dark Data – An Interview with Markus Lindelow of Iron Mountain

Information Governance Perspectives

I interviewed him this November to discuss his thoughts on the evolution of metadata, content classification, AI, and how companies are using the new pillars of data science to break down their silos, help customers get lean and discover the hidden values in their big data sets.

article thumbnail

Achieve your AI goals with an open data lakehouse approach

IBM Big Data Hub

Typically, on their own, data warehouses can be restricted by high storage costs that limit AI and ML model collaboration and deployments, while data lakes can result in low-performing data science workloads. New insights and relationships are found in this combination. All of this supports the use of AI.

article thumbnail

Augmented data management: Data fabric versus data mesh

IBM Big Data Hub

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale.