Remove Cost-Benefit Remove Experimentation Remove Modeling Remove Risk
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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

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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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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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7 steps for turning shadow IT into a competitive edge

CIO Business Intelligence

Ask IT leaders about their challenges with shadow IT, and most will cite the kinds of security, operational, and integration risks that give shadow IT its bad rep. That’s not to downplay the inherent risks of shadow IT.

IT 137
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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. Davé and his team’s achievements in AI are due in large part to creating opportunities for experimentation — and ensuring those experiments align with CBRE’s business strategy. Let’s start with the models. And those experiments have paid off.

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The path to socially responsible AI

CIO Business Intelligence

People across industries envisioned AI as being the fuel for thousands of hours of time saved, replacing jobs to reduce costs, and unlocking innovation at a scale we have never before seen. Interacting with AI data or experiences in silos introduces risk and increases inefficiencies because it’s kept separate.

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3 key digital transformation priorities for 2024

CIO Business Intelligence

Meanwhile, CIOs must still reduce technical debt, modernize applications, and get cloud costs under control. Many technology investments are merely transitionary, taking something done today and upgrading it to a better capability without necessarily transforming the business or operating model.