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Demand for end-to-end platforms, the convergence of data and AI, and the evolution of roles in the generative AI era are all driving the change, say analysts.
Microsoft’s new generative AI-powered, multimodal, content analysis service is a next-generation version of its existing Cognitive Services platform.
What’s the best way to store, search, and analyze content not based on their technical characteristics but on their meaning?
The Apache Kafka, Apache Flink, and Apache Iceberg communities are developing new ways for engineers to manage data and meet application needs.
Dataframes are a staple element of data science libraries and frameworks. Here's why many developers prefer them for working with in-memory data.
Data science and dataops have a critical role to play in developing revenue forecasts business leaders can count on.
PipelineDP4j, an ‘out-of-the-box’ solution for analyzing data sets in Apache Beam and Apache Spark in a privacy-preserving way, is intended to be usable by all developers.
Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those discrepancies.
Do you need to distribute a heavy Python workload across multiple CPUs or a compute cluster? These seven frameworks are up to the task.
Dynamic language built for fast numerical computing introduces lower-level alternative to Array that delivers significant speedups and more maintainable code.