Remove Artificial Inteligence Remove Metrics Remove Off-The-Shelf
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CIOs’ lack of success metrics dooms many AI projects

CIO

Many organizations have launched dozens of AI proof-of-concept projects only to see a huge percentage fail, in part because CIOs don’t know whether the POCs are meeting key metrics, according to research firm IDC. The potential cost can be huge, with some POCs costing millions of dollars, Saroff says.

Metrics 187
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What to expect from AI in the enterprise in 2025

CIO

This is particularly true with enterprise deployments as the capabilities of existing models, coupled with the complexities of many business workflows, led to slower progress than many expected. But this isnt intelligence in any human sense. With AI, this means augmenting your existing skills base and leveraging your human assets.

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Bandit ML helps e-commerce businesses present the most effective offer to each shopper

TechCrunch

The idea, as he explained via email, is that one customer might be more excited about a $5 discount, while another might be more effectively enticed by free shipping, and a third might be completely uninterested because they just made a large purchase. The startup was part of the summer 2020 class at accelerator Y Combinator.

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Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

AWS Machine Learning - AI

SQL is one of the key languages widely used across businesses, and it requires an understanding of databases and table metadata. This application allows users to ask questions in natural language and then generates a SQL query for the users request. However, off-the-shelf LLMs cant be used without some modification.

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When is data too clean to be useful for enterprise AI?

CIO

Organizations using their own codebase to teach AI coding assistants best practices need to remove legacy code with patterns they don’t want repeated, and a large dataset isn’t always better than a small one. “One But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211
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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning - AI

Generative artificial intelligence (AI) applications powered by large language models (LLMs) are rapidly gaining traction for question answering use cases. This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application.

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Deploy foundation models with Amazon SageMaker, iterate and monitor with TruEra

AWS Machine Learning - AI

These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing images and videos. Despite the great generalization capabilities of these models, there are often use cases where these models have to be adapted to new tasks or domains.