Remove Business Objectives Remove Experimentation Remove Modeling
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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. Experiments allow AI PMs not only to test assumptions about the relevance and functionality of AI Products, but also to understand the effect (if any) of AI products on the business. Don’t expect agreement to come simply.

Marketing 363
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.

Strategy 290
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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. Counter claims?

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

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What high-performance IT teams look like today — and how to build one

CIO Business Intelligence

Our mental models of what constitutes a high-performance team have evolved considerably over the past five years. High performance back then generally focused on delivery — a contrast to previous generations of IT where business and IT alignment was an issue, and teams struggled to deliver with waterfall project management practices.

IT 141
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Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.

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6 enterprise DevOps mistakes to avoid

CIO Business Intelligence

Rick Boyce, CTO at AND Digital, underscores how a typical IT project mentality toward DevOps can undercut the CIO’s ability to deliver on business objectives. CIOs may mistakenly underinvest in practices that improve user experiences, increase alignment with business stakeholders, and promote a positive developer experience.