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For machine learning to be used more widely, it needs to be brought closer to the data that fuels ML modeling and insights. For database developers and data analysts that are still getting up to speed on ML modeling, the ideal scenario is to integrate ML algorithms and training models directly into the tools they already use, making it easier for them to extract meaningful insights from their data.
In-house database teams are likely to be experts in SQL, but they may not know Python, which has emerged as a primary programming language for AI and machine learning. As a result, they’re reliant on data scientists to build the models for them to add intelligence to their applications.
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Graph-Based AI Enters the Enterprise Mainstream
Graph AI is becoming fundamental to anti-fraud, sentiment monitoring, market segmentation, and other applications where complex patterns must be rapidly identified.
Artificial intelligence (AI) is one of the most ambitious, amorphous, and comprehensive visions in the history of automated information systems.
Fundamentally, AI’s core approach is to model intelligence or represent knowledge so that it can be executed algorithmically in general-purpose or specialized computing architectures. AI developers typically build applications through an iterative process of constructing and testing knowledge-representation models to optimize them for specific outcomes.
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