In this article, we give you some tips to successfully transform Mobile DevOps processes in your e-commerce app and to minimize your revenue loss.

Modern-day applications aim to cover a wide range of users’ needs in a flexible and scalable manner. The software development landscape is moving at a fast pace, and the competition is so fierce that a business simply can’t afford to offer a low-quality product. Thorough testing of both functional and non-functional aspects of a software application has become an essential step in the software development process. It ensures that users get the best version of your product.

DevOps literally is a combination of two words- Development and Operations. It is basically a software development methodology that integrates all the Software Development functions from development to operations within the same cycle.

In this blog post we’ll help answer the age old question, “What does this service talk to and what does it say?” We’ll see how to inspect inbound and outbound REST API calls to see what calls are being made and what incoming traffic causes a reaction. This can be pretty handy when you’re taking over maintenance of an existing service, or if your code just isn’t behaving the way you expect.

From the traditional Waterfall model to more iterative approaches like Agile and DevOps, software testing is constantly evolving. And while teams have worked their way to deliver quality at speed, there seems to be something holding them back. Read on to learn about in-sprint automation and why it’s the key to moving at DevOps speed.

Here at Ably we consider software development to be in our DNA. We write and maintain a lot of code, much of it open-source, with full public visibility, and we host and manage it using the GitHub platform. The code is segmented into numerous discrete repositories, each with a specific scope and purpose. We've been using GitHub heavily since Ably was founded in 2016, so we have plenty of history, and we're committed to the platform.

It’s easy to take continuous integration (CI) and continuous delivery/deployment (CD) for granted these days, but these have been transformational concepts that have drastically changed the face of software development over the past thirty years.

While CI/CD is synonymous with modern software development best practices, today’s machine learning (ML) practitioners still lack similar tools and workflows for operating the ML development lifecycle on a level on par with software engineers. For background, follow a brief history of transformational CI/CD concepts and how they’re missing from today’s ML development lifecycle.