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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 363
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CBRE’s Sandeep Davé on accelerating your AI ambitions

CIO Business Intelligence

Sandeep Davé knows the value of experimentation as well as anyone. CBRE has also used AI to optimize portfolios for several clients, and recently launched a self-service generative AI product that enables employees to interact with CBRE and external data in a conversational manner. Let’s start with the models.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

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How Italian CIOs produce value with gen AI

CIO Business Intelligence

Creating new business models Gen AI is also unique in that it can generate useful business models. Some gen AI applications can already summarize customer voice and written interactions with the contact center, or, in marketing and sales, identify new sales leads from calls. AI is the future for us,” says Maffei.

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The top 15 big data and data analytics certifications

CIO Business Intelligence

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They can also transform the data, create data models, visualize data, and share assets by using Power BI.

Big Data 125
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Retailers can tap into generative AI to enhance support for customers and employees

IBM Big Data Hub

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications. The impact of these investments will become evident in the coming years.

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Getting ready for artificial general intelligence with examples

IBM Big Data Hub

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.