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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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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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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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CIOs sharpen cloud cost strategies — just as gen AI spikes loom

CIO Business Intelligence

Yet, controlling cloud costs remains the top challenge IT leaders face in making the most of their cloud strategies, with about one third — 35% — of respondents citing these expenses as the No. 1 barrier to moving forward in the cloud. However, as these environments grow and become more complex, the challenges persist.”

Strategy 140
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10 Essential Data-Driven B2B Email Marketing Strategies

Smart Data Collective

A growing number of marketers are exploring the benefits of big data as they strive to improve their branding and outreach strategies. If you want to make the most of your big data strategy, you should keep reading to learn how to incorporate data into email marketing. How to Use Data to Improve Your Email Marketing Strategy.

B2B 118
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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). It is also a sound strategy when experimenting with several parameters at the same time.

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What you need to know about product management for AI

O'Reilly on Data

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. AI product estimation strategies.