Understanding automation

Decision makers are busy. Businesses can hire people to analyze data for them, but most companies are resource constrained and cant hire a small army to look through all their data. Wayde Fleener explains how General Mills implemented automation to enable decision makers to quickly focus on the metrics that matter and cut through everything else that does not.

Divya Ramachandran explains how top insurance companies have used handwriting transcription powered by deep learning to achieve a more than 70% reduction in daily operational processing time, develop a best-in-industry predictive model for assessing mortality risk from decades of archived forms, and enable a smarter claims leakage review, which led to a 10x ROI in its first year.

Before the arrival of Cloud Native, IT departments frequently treated each component–a service/application, a virtual machine, or a bare metal server–as a special, fragile entity that required the u …

Georgios Gkekas shares ING's advanced analytics journey to promote modern machine and deep learning techniques internally through a central, best-of-breed technical platform tailored for data science activities. The platform offers only the necessary automated tools to replace the tedious, repetitive, and error-prone steps in a typical data science pipeline.

Chaos engineering leverages carefully designed failure injection tests and the distributed automation inherent in cloud deployments to prove that there is enough margin to absorb failures in production. Adrian Cockcroft outlines the overall architectural principles of chaos engineering and shares methods engineers can use to exercise failure modes in safety and business-critical systems.

Philippe Guerin demonstrates how to transform a monolithic application into a microservices application while tracking the change of the transformation and balancing the loss of performance or stability of your application with the introduction of the new layers.