Artificial Inteligence Culture Data Engineering Machine Learning 
CIO
SEPTEMBER 29, 2022
s SVP and chief data & analytics officer, has a crowâ??s s nest perspective of immediate and long-term tasks to equally strengthen the company culture and customer needs. s unique about the [chief data officer] role is it sits at the cross-section of data, technology, and analytics,â?? re getting excited about.
CIO
APRIL 12, 2023
Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence. Real-time AI involves processing data for making decisions within a given time frame. It isn’t easy.
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CIO
JUNE 10, 2024
As head of transformation, artificial intelligence, and delivery at Guardian Life, John Napoli is ramping up his company’s AI initiatives. Moreover, many need deeper AI-related skills, too, such as for building machine learning models to serve niche business requirements. Here’s how IT leaders are coping.
InfoQ Culture Methods
FEBRUARY 25, 2021
Machine learning is a powerful new tool, but how does it fit in your agile development? Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. By Jay Palat.
InfoQ Culture Methods
FEBRUARY 16, 2023
Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers itself. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make.
InfoQ Culture Methods
JULY 28, 2020
To become a machine learning engineer, you have to interview. You have to gain relevant skills from books, courses, conferences, and projects. Include technologies, frameworks, and projects on your CV. In an interview, expect that you will be asked technical questions, insight questions, and programming questions.
d2iq
FEBRUARY 19, 2021
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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