Remove Advertising Remove Data Collection Remove Experimentation Remove Testing
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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362
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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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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.

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Eight Silly Data Things Marketing People Believe That Get Them Fired.

Occam's Razor

PS: The phrase "real-time data analysis" is an oxymoron. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. PS: Bonus : Facebook Advertising / Marketing: Best Metrics, ROI, Business Value. We use that on very thin ice data, we bought advertising.

Marketing 166
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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

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Best Web Analytics 2.0 Tools: Quantitative, Qualitative, Life Saving!

Occam's Razor

If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. If you have evolved to a stage that you need behavior targeting then get Omniture Test and Target or Sitespect. Move from a data collection obsession and develop a crush on data analysys.

Analytics 135