Remove Experimentation Remove Management Remove Metrics
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Practical Skills for The AI Product Manager

O'Reilly on Data

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI products. The AI Product Pipeline.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools.

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Corinium Meets: Quantum Metric Head of Behavioural Research Marina Shapira

Corinium

Ahead of her presentation at CDAO UK, we spoke with Quantum Metric’s Marina Shapira about predictive analytics, why companies should embrace a culture of experimentation how and CAOs and CXOs can work together effectively. What is behavioural research? And what role should it play in an organization's data and analytics strategy?

Metrics 300
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AI Product Management After Deployment

O'Reilly on Data

The field of AI product management continues to gain momentum. As the AI product management role advances in maturity, more and more information and advice has become available. One area that has received less attention is the role of an AI product manager after the product is deployed.

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How to Build an Experimentation Culture for Data-Driven Product Development

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. Attendance of this webinar will earn one PDH toward your NPDP certification for the Product Development and Management Association. Save your seat for this exclusive webinar today!

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10 AI strategy questions every CIO must answer

CIO Business Intelligence

The time for experimentation and seeing what it can do was in 2023 and early 2024. At Vanguard, we are focused on ethical and responsible AI adoption through experimentation, training, and ideation, she says. I dont think anyone has any excuses going into 2025 not knowing broadly what these tools can do for them, Mason adds.

Strategy 141
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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. That metric is tied to a KPI.

Metrics 157