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From project to product: Architecting the future of enterprise technology

CIO Business Intelligence

Understanding and tracking the right software delivery metrics is essential to inform strategic decisions that drive continuous improvement. Wikipedia defines a software architect as a software expert who makes high-level design choices and dictates technical standards, including software coding standards, tools, and platforms.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Most teams approach this like traditional software development but quickly discover it’s a fundamentally different beast. Check out the graph belowsee how excitement for traditional software builds steadily while GenAI starts with a flashy demo and then hits a wall of challenges? Whats worse: Inputs are rarely exactly the same.

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Generative AI: A Self-Study Roadmap

KDnuggets

Instead of optimizing for accuracy metrics, you evaluate creativity, coherence, and usefulness. Concepts like overfitting, generalization, and evaluation metrics translate directly to generative AI, though the specific metrics differ. Probability and Statistics : Generative models are probabilistic systems.

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Monitoring Data Without Turning into Big Brother

Smart Data Collective

You are not alone if your business handles large sets of digital records or metrics. Add Context to Every Metric How can the best work tracking app help add context to productivity metrics? Personalize What Productivity Looks Like How can employee monitoring software personalize performance standards? All Rights Reserved.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries. Like software, data products should have versioning and changelogs to track evolution and impact. This article was made possible by our partnership with the IASA Chief Architect Forum.

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Agentic AI design: An architectural case study

CIO Business Intelligence

Lastly, the system needs to keep track of the number of records in each file, the time it takes to create the output, the time it takes to process, the number of errors created per output test file by the 12 different test types, the number of errors correctly captured by the automated tests and other business-specific metrics.

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From fixed frameworks to strategic enablers: Architecting AI transformation

CIO Business Intelligence

Implement outcome-based metrics : Measure architectural success through business outcomes rather than technical compliance. Develop new skills and competencies : Invest in architectural talent that combines technical expertise with strategic business acumen to lead AI transformation.