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Navigating The Complex Waters of Insurance Executive Search: A Strategic Guide

N2Growth Blog

Crafting the Future: The Significance of Selecting the Right Insurance Executive In today’s fast-paced and ever-evolving business environment, securing the right insurance executive is more than a mere hiring decision —it’s a pivotal investment in the company’s future.

Insurance 164
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Machine Learning: Unlocking the Next for Insurers

Coforge

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Southeast Asia insurtech Igloo increases its Series B to $46M

TechCrunch

Igloo develops its insurance products and then partners with insurers who underwrite their policies. Igloo currently works with 20 global, regional and local insurers across Southeast Asia. It distributes its insurance products through partnerships, and is partnered with over 55 companies in 7 countries.

Insurance 236
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Space-Based AI Shows the Promise of Big Data

Cloudera

Although researchers can recruit “citizen scientists” to help look at images through crowdsourcing ventures such as Zooniverse , astronomy is turning to artificial intelligence (AI) to find the right data as quickly as possible. It’s a process that some companies call geospatial intelligence (GI). .

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Ever Smarter Software: The Ultimate Introduction to Machine Learning

UruIT

Intimidated by Artificial Intelligence? We’ll break it down in this Introduction to Machine Learning Guide. From enhancing user experiences to achieving greater operational excellence, the use of artificial intelligence is ubiquitous. Looking for some help with your next machine learning/AI project?

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Artificial intelligence is still in its infancy. Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. So what should an organization keep in mind before implementing a machine learning solution?

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MLOps: Methods and Tools of DevOps for Machine Learning

Altexsoft

When speaking of machine learning, we typically discuss data preparation or model building. Living in the shadow, this stage, according to the recent study , eats up 25 percent of data scientists time. MLOps lies at the confluence of ML, data engineering, and DevOps. More time for development of new models.