Systems | Development | Analytics | API | Testing

“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.

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.

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.

If you write software for a living, you probably have a bias toward coded tests and against all things codeless. Most software engineers who become test engineers trust themselves to write well-designed structured code. Some people see record-and-playback as cheating, demeaning, or otherwise indicative of poor workmanship. Yet, research shows that test code maintenance costs correlate directly to the number of lines of written test code.

Aside from ensuring each service is working properly, one of the most challenging parts of managing a cloud-based infrastructure is cost monitoring. There are countless services to keep track of—including storage, databases, and computation—each with their own complex pricing structure. Monitoring cloud costs is quite different from other organizational costs in that it can be difficult to detect anomalies in real-time and accurately forecast monthly costs.