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Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

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How AI orchestration has become more important than the models themselves

CIO Business Intelligence

To integrate AI into enterprise workflows, we must first do the foundation work to get our clients data estate optimized, structured, and migrated to the cloud. Once the data foundation is in place, it is important to then select and embed the best combination of AI models into the workflow to optimize for cost, latency, and accuracy.

Modeling 116
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The key to operational AI: Modern data architecture

CIO Business Intelligence

While in the experimentation phase, speed is a priority, the implementation phase requires more attention to resiliency, availability, and compatibility with other tools. As a result, ​developers — regardless of their expertise in machine learning — will be able to develop and optimize business-ready large language models (LLMs).

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? Get ready to discover how these innovative approaches not only overcome the limitations of traditional A/B testing, but also unlock new insights and opportunities for optimization!

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Gartner projects major IT spending increases for 2025

CIO Business Intelligence

By 2026, hyperscalers will have spent more on AI-optimized servers than they will have spent on any other server until then, Lovelock predicts. Forrester also recently predicted that 2025 would see a shift in AI strategies , away from experimentation and toward near-term bottom-line gains. Next year, that spending is not going away.

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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. However, none of these layers help with modeling and optimization. This approach is not novel. Model Operations.

IT 364