Remove Experimentation Remove Measurement Remove Reference
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MCP, ACP, and Agent2Agent set standards for scalable AI results

CIO Business Intelligence

Open protocols aimed at standardizing how AI systems connect, communicate, and absorb context are providing much needed maturity to an AI market that sees IT leaders anxious to pivot from experimentation to practical solutions. I affectionately refer to MCP as the plumbing stack. It also helps them to avoid vendor lock-in, he adds.

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Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

What this meant was the emergence of a new stack for ML-powered app development, often referred to as MLOps. ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). How will you measure success? The answers were: Our students.

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From project to product: Architecting the future of enterprise technology

CIO Business Intelligence

By articulating fitness functions automated tests tied to specific quality attributes like reliability, security or performance teams can visualize and measure system qualities that align with business goals. Experimentation: The innovation zone Progressive cities designate innovation districts where new ideas can be tested safely.

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Escorts Kubota enlists AI to reinvent railway, construction, and agriculture

CIO Business Intelligence

Kubota has projects across these pillars in various stages of maturity, with some already live and some still in experimentation. Kakkar’s litmus test for pursuing a project depends on whether it has a clear purpose, goal, and measurable objectives. Kakkar says that they created complete mapping access for everyone’s reference. “We

IoT
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A Field Guide to Rapidly Improving AI Products

O'Reilly on Data

Heres a common scene from my consulting work: AI TEAM Heres our agent architectureweve got RAG here, a router there, and were using this new framework for ME [Holding up my hand to pause the enthusiastic tech lead] Can you show me how youre measuring if any of this actually works? Instead, they obsess over measurement and iteration.

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What you need to know about product management for AI

O'Reilly on Data

There may even be someone on your team who built a personalized video recommender before and can help scope and estimate the project requirements using that past experience as a point of reference. It’s difficult to be experimental when your business is built on long-term relationships with customers who often dictate what they want.

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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. but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise.

IT