“You cannot be the same, think the same and act the same if you hope to be successful in a world that does not remain the same.” This sentence by John C. Maxwell is so relevant to rapidly changing cloud hosting technology. Businesses understand the added value and are looking at cloud technologies to handle both operational and analytical workloads.
At Kong, I get a chance to discuss with various organizations their plans and projects to adopt microservices and expose them with APIs. During these discussions, I’ve started to recognize some patterns that appear with regularity – patterns that have less to do with technology than with people. Technologists and engineers like myself usually do not pay too much attention to the “softer” aspects of technology implementations.
Employees today are more mobile than ever. As we saw, due to COVID-19 the majority of organizations moved their employees to a work from home model overnight. This quick change of location forced businesses to implement solutions that would provide their workforces secure remote access to an increasingly complex corporate network.
This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.
With over 1.5 million members, Touring Club Suisse is the largest mobility club in Switzerland. Founded in 1896, TCS provides assistance, road safety education and accident prevention to all travelers and vehicles along the entire Swiss road infrastructure. TCS adopted Xray for their test management in 2015, which allowed them to manage all the testing activity in Jira and have a simple and solid QA and release process all in Jira.
