How Insurance Compares: Benchmarking Digital and API Best Practices

The Insurance industry is risk averse by its very nature. However, with a 10x increase in Insurance technology investment between 2010-2015, is the industry about to push through its own digital revolution? Join Tony Cassin-Scott, Insurance Data, Digital and IT Strategist, and Jerome Bugnet, Insurance API Specialist, MuleSoft, on December 14 at 10am (GMT) for a live webinar as they discuss how the world’s most progressive insurers are embracing digital transformation and APIs (Application Program Interface) to build and deploy industry-specific software applications, and how this compares against their peers in other industries.
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Spotlight

OTHER ON-DEMAND WEBINARS

Powering Your People with AR

top 5 industrial use cases for leveraging augmented reality on smart glasses. Hosted by our VP of Customer Solutions, Aaron Tate, and Senior Director of Product, Chris Delvizis, this webinar covered: When and where smart glasses with AR can impact your operations, Compelling use cases in manufacturing, logistics, and field service and How companies are currently deploying smart glasses
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Phishing Protection and Education – Anytime, Any Place, Anywhere

WatchGuard

Phishing is one of the greatest security threats facing organizations of all sizes. But as more and more of your employees are leaving the network, you need a way to protect them from this type of attack wherever business takes them. Learn more about new ways to educate your employees before, during, and after a phishing attack, as well as the defense you need to have in place to defend against them. In this on-demand recording, we will cover: Security challenges of the mobile workforce.
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The Untapped Potential of BIM in the Field

Procore

BIM isn't just for the office anymore. In this webinar, we'll explore how to take the power of BIM and put it in the hands of the field. Listen to our panel of experts discuss how to harness the potential of the model to boost productivity, quality, and safety.
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Scaling Machine Learning Workloads with Ray

Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray was created in the UC Berkeley RISELab to make it easy for every engineer to scale their applications and ML workloads, without requiring any distributed systems expertise, making distributed programming easy.
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