Remove Cost-Benefit Remove Experimentation Remove Optimization
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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362
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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 to Set AI Goals

O'Reilly on Data

AI Benefits and Stakeholders. AI is a field where value, in the form of outcomes and their resulting benefits, is created by machines exhibiting the ability to learn and “understand,” and to use the knowledge learned to carry out tasks or achieve goals. AI-generated benefits can be realized by defining and achieving appropriate goals.

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Your New Cloud for AI May Be Inside a Colo

CIO Business Intelligence

Enterprises moving their artificial intelligence projects into full scale development are discovering escalating costs based on initial infrastructure choices. Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex.

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GenAI sticker shock sends CIOs in search of solutions

CIO Business Intelligence

The early bills for generative AI experimentation are coming in, and many CIOs are finding them more hefty than they’d like — some with only themselves to blame. By understanding their options and leveraging GPU-as-a-service, CIOs can optimize genAI hardware costs and maintain processing power for innovation.”

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The key to operational AI: Modern data architecture

CIO Business Intelligence

To fully benefit from AI, organizations must take bold steps to accelerate the time to value for these applications. While in the experimentation phase, speed is a priority, the implementation phase requires more attention to resiliency, availability, and compatibility with other tools. This is where Operational AI comes into play.

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Amazing Data-Driven Software Applications Revolutionizing Inventory Optimization

Smart Data Collective

One of the most important applications of big data technology lies with inventory management and optimization. Understanding the Best Data-Driven Inventory Optimization Applications for the Coming Year. This is the best inventory optimization software for 2021, according to the latest research updated in December 2020 by Business.org.