Remove Deep Learning Remove Experimentation Remove Optimization
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The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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Generative AI: A Self-Study Roadmap

KDnuggets

Traditional machine learning systems excel at classification, prediction, and optimization—they analyze existing data to make decisions about new inputs. Instead of optimizing for accuracy metrics, you evaluate creativity, coherence, and usefulness. This difference shapes everything about how you work with these systems.

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Synthetic data’s fine line between reward and disaster

CIO Business Intelligence

It can even be used for controlled experimentation, assuming you can make it accurate enough. Sufficiently accessible and accurate tools could deliver operational improvements and revenue, as well as optimized costs and reduced risks in many areas of business decision making.

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Your data’s wasted without predictive AI. Here’s how to fix that

CIO Business Intelligence

This is where we blend optimization engines, business rules, AI and contextual data to recommend or automate the best possible action. Think of the next-best-offer algorithms in e-commerce, dynamic hospitality pricing or logistics route optimization. These capabilities are no longer theoretical or experimental.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. Not only is data larger, but models—deep learning models in particular—are much larger than before. Model Operations.

IT
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

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What you need to know about product management for AI

O'Reilly on Data

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).