One key to efficient data analysis of big data is to do the computations where the data lives. In some cases, that means running R, Python, Java, or Scala programs in a database such as SQL Server or ...

There are still a lot of obstacles to building machine learning models and one of those is that in order to build those models, developers often have to move a lot of data back and forth between their ...

Among the many announcements from last week’s Google Cloud Next event was a noteworthy update to Google LLC’s BigQuery, adding machine learning capabilities to the SQL data warehousing tool. The ...

AI is shaping every field by making skills (such as coding or data visualization) accessible to everyone, which weren’t available in the past. An AI operator who can run the right prompts can perform ...

Two of the greatest obstacles to getting started with today’s deep learning systems have been the lack of truly “point and click” interfaces to creating new models and the immense complexities in ...

Google is trying to foster more collaboration within the machine learning community, which is focused on a specific subset of artificial intelligence that tries to mimic the way the human brain learns ...

Google's new product changes add another valuable business intelligence integration in the marketplace. The best business solutions are like Jenga pieces — you need the right pieces to hold the stack ...

At it Cloud Next conference in San Francisco today, Google is announcing new machine learning (ML) capabilities in its BigQuery cloud data warehouse service. This new service within a service is, ...

Google is abandoning its homegrown SQL variant as the recommended default query language for its BigQuery service in favor of a new standard-compliant dialect in the works for the managed data ...