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How AI orchestration has become more important than the models themselves

CIO Business Intelligence

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. million on inference, grounding, and data integration for just proof-of-concept AI projects.

Modeling 116
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Amazon Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools

AWS Big Data

Amazon Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.

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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278
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Why you should care about debugging machine learning models

O'Reilly on Data

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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Data & Analytics Maturity Model Workshop Series

Speaker: Dave Mariani, Co-founder & Chief Technology Officer, AtScale; Bob Kelly, Director of Education and Enablement, AtScale

Workshop video modules include: Breaking down data silos. Integrating data from third-party sources. Developing a data-sharing culture. Combining data integration styles. Translating DevOps principles into your data engineering process. Using data models to create a single source of truth.

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A Comprehensive Guide on Langchain

Analytics Vidhya

Introduction Large language models (LLMs) have revolutionized natural language processing (NLP), enabling various applications, from conversational assistants to content generation and analysis. However, working with LLMs can be challenging, requiring developers to navigate complex prompting, data integration, and memory management tasks.

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Enabling AI with real-time data integration

CIO Business Intelligence

An accumulation of transactions, customer information, operational data, and all sorts of other information, it holds a tremendous amount of value. That data, when paired with artificial intelligence (AI) models, can give businesses new insights into the way they make decisions and where to find opportunities for growth.