Systems | Development | Analytics | API | Testing

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.

Anyone who's worked in technology has likely hit on the "build vs. buy" question at some point. Should you build your own custom solution to meet the exact requirements of your business? Or does it make more financial sense and save time if you use a third-party vendor? Let's use an example. You are a tech lead at a company that delivers a platform for online learning.

This blog is an attempt to help you understand DevOps and MLOps and their differences and similarities.