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5 findings from O'Reilly's machine learning adoption survey companies should know

O'Reilly Media - Data

New survey results highlight the ways organizations are handling machine learning's move to the mainstream. As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work. What metrics are used to evaluate success?

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Remember when developers reigned supreme? The market for software coding goes soft

CIO

Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand. An example of the new reality comes from Salesforce.

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Practical Skills for The AI Product Manager

O'Reilly Media - Ideas

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.

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What you need to know about product management for AI

O'Reilly Media - Ideas

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). You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. Machine learning adds uncertainty.

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AI Product Management After Deployment

O'Reilly Media - Ideas

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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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization. “Given these challenges, organizations today need to choose between two flawed approaches when it comes to developing machine learning. .

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What is Machine Learning Engineer: Responsibilities, Skills, and Value Brought

Altexsoft

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.