Remove Data Engineering Remove Fractional CTO Remove Machine Learning
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IT leaders: What’s the gameplan as tech badly outpaces talent?

CIO

Dun and Bradstreet has been using AI and ML for years, and that includes gen AI, says Michael Manos, the companys CTO. In a November report by HR consultancy Randstad, based on a survey of 12,000 people and 3 million job profiles, demand for AI skills has increased five-fold between 2023 and 2024. Other surveys found a similar gap.

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What is data architecture? A framework to manage data

CIO

Many organizations today are looking to modernize their data architecture as a foundation to fully leverage AI and enable digital transformation. Consulting firm McKinsey Digital notes that many organizations fall short of their digital and AI transformation goals due to process complexity rather than technical complexity.

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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. The job will evolve as most jobs have evolved.

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10 key roles for AI success

CIO

Successful AI teams also include a range of people who understand the business and the problems it’s trying to solve, says Bradley Shimmin, chief analyst for AI platforms, analytics, and data management at consulting firm Omdia. Data scientists are the core of any AI team. Data engineer.

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IT leaders get creative to fill data science gaps

CIO

That is backed up by a 2021 survey by industry analysts at Forrester, which showed that, of 2,329 data and analytics decision-makers worldwide, 55% want to hire data scientists. And machine learning engineers are being hired to design and build automated predictive models. More advanced companies get that.

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AI Chihuahua! Part I: Why Machine Learning is Dogged by Failure and Delays

d2iq

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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What I have been working on: Modal

Erik Bernhardsson

Most of my career has been in data. I then spent six years as a CTO, although I managed the data team directly for a long time and would occasionally write some data code. Data 1 strikes me a a discipline that deserves a bit more love. Data as its own discipline. This seems… wasteful?

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