Remove Experimentation Remove Marketing Remove Metrics Remove Presentation
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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. Agreeing on metrics. Identifying the problem.

Marketing 363
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What you need to know about product management for AI

O'Reilly on Data

You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. In the best case scenario, the trained neural network accurately represents the underlying phenomenon of interest and produces the correct output even when presented with new input data the model didn’t see during training.

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Digital transformation’s fundamental change management mistake

CIO Business Intelligence

Organizations with long, complex, and expensive upfront planning processes due to executive and stakeholder alignment issues can result in missed opportunities if competitors bring capabilities to market faster. It’s like trying to get a jazz quartet, a rock band, a classical orchestra, and a DJ to play in harmony.”

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Rebranding IT for the modernized IT mission

CIO Business Intelligence

What that means differs by company, and here are a few questions to consider on what the brand and mission should address depending on business objectives: Is IT taking on more front-office responsibilities, including building products and customer experiences or partnering with sales and marketing on their operations and data needs?

IT 80
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3 AI Trends from the Big Data & AI Toronto Conference

DataRobot Blog

Model Observability – the ability to track key health and service metrics for models in production – remains a top priority for AI-enabled organizations. Other use cases, such as marketing campaigns, need to run on large quantities of data, but latency isn’t particularly an issue. DataRobot Booth at Big Data & AI Toronto 2022.

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Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

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Digital listening reveals 3 leading innovation drivers

CIO Business Intelligence

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.