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IDC chief research officer: GenAI, from experimentation to adoption

CIO Business Intelligence

Its been a year of intense experimentation. Now, the big question is: What will it take to move from experimentation to adoption? The key areas we see are having an enterprise AI strategy, a unified governance model and managing the technology costs associated with genAI to present a compelling business case to the executive team.

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A Guide to Flax: Building Efficient Neural Networks with JAX

Analytics Vidhya

Flax is an advanced neural network library built on top of JAX, aimed at giving researchers and developers a flexible, high-performance toolset for building complex machine learning models. This blog […] The post A Guide to Flax: Building Efficient Neural Networks with JAX appeared first on Analytics Vidhya.

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I Tried All the Latest Google 2.0 Model APIs for Free

Analytics Vidhya

models, bringing substantial upgrades to their chatbot and developer tools. Pro (experimental), and the new cost-efficient Gemini 2.0 Model APIs for Free appeared first on Analytics Vidhya. Pro (experimental), and the new cost-efficient Gemini 2.0 Model APIs for Free appeared first on Analytics Vidhya.

Modeling 140
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AI data readiness: C-suite fantasy, big IT problem

CIO Business Intelligence

Confidence from business leaders is often focused on the AI models or algorithms, Erolin adds, not the messy groundwork like data quality, integration, or even legacy systems. But 84% of the IT practitioners surveyed, including data scientists, data architects, and data analysts, spend at least one hour a day fixing data problems.

IT 134
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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. However, the concept is quite abstract. Can’t we just fold it into existing DevOps best practices? Why: Data Makes It Different.

IT 364
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An Architecture of Participation for AI?

O'Reilly on Data

About six weeks ago, I sent an email to Satya Nadella complaining about the monolithic winner-takes-all architecture that Silicon Valley seems to envision for AI, contrasting it with the architecture of participation that had driven previous technology revolutions, most notably the internet and open source software.

Marketing 247
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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. From Llama3.1 to Gemini to Claude3.5 From Llama3.1 to Gemini to Claude3.5

Modeling 116