#datamining-discoveries

#datamining-discoveries

Wiggins will lead a team that optimizes the use of machine learning and artificial intelligence to improve outcomes company-wide, from maximizing advertising and subscriber revenue to creating unique and personalized experiences for users.

"In agentic environments, agents mutate state across data, systems, and configurations in ways that compound fast and are hard to trace," says Pranay Ahlawat, Chief Technology and AI Officer at Commvault.

Galileo provides AI teams with tools to evaluate the quality of AI outputs, detect errors before they reach end users, and continuously improve the behavior of AI agents in production.

Developers have spent the past decade trying to forget databases exist. Not literally, of course. We still store petabytes. But for the average developer, the database became an implementation detail; an essential but staid utility layer we worked hard not to think about. We abstracted it behind object-relational mappers (ORM). We wrapped it in APIs. We stuffed semi-structured objects into columns and told ourselves it was flexible.

Weather impacts sales. Every retailer knows it. But for most, the likelihood that it might rain, snow, or sleet on the third of March somewhere in the Midwest is rarely used. Vendors such as Weather Trends have offered accurate, long-range forecasts for more than 20 years. But the opportunity is not predicting the weather; it's knowing what to do with the data. AI might change that.

The title "data scientist" is quietly disappearing from job postings, internal org charts, and LinkedIn headlines. In its place, roles like "AI engineer," "applied AI engineer," and "machine learning engineer" are becoming the norm. This Data Scientist vs AI Engineer shift raises an important question for practitioners and leaders alike: what actually changes when a data scientist becomes an AI engineer, and what stays the same? More importantly, what skills matter if you want to make this transition intentionally rather than by accident?

The rise of generative AI is often seen as an existential threat to the SaaS model. Interfaces would disappear, software would fade away, and existing players would become irrelevant. However, new figures from Databricks paint a different picture. Rather than undermining SaaS, AI appears to be increasing its use. This week, Databricks reported a revenue run rate of $5.4 billion, a 65 percent year-on-year increase. More than a quarter of that now comes from AI-related products.