If your experience as a developer is anything like mine, the best moments are those known as the "flow state.” When distractions drift into the background and all your energy is going in the creative direction of solving the problem at hand. Your brain is directly connected to your users through your code. Months of progress happen in hours. Unfortunately, those moments are rare. But it’s not emails or Slack messages that are the biggest distractions.
Imagine you’ve been working on a new feature for weeks. Finally—after exhaustive QA and testing, and more late coding sessions than you care to count—you release with a feeling of both accomplishment and relief. Only to be woken up at 3am that something’s wrong. Or worse, you get flooded with customer complaints that it’s not working properly. Well, it’s probably a scenario you don’t need to imagine. Every developer has a version of this story to tell.
One of the many areas where machine learning has made a large difference for enterprise business is in the ability to make accurate predictions in the realm of fraud detection. Knowing that a transaction is fraudulent is a critical requirement for financial services companies, but knowing that a transaction that was flagged by a rules-based system as fraudulent is a valid transaction, can be equally important.
It’s been 24 years since Jerry Maguire hit movie theaters in 1996, and yet one line from the movie still resonates like no other.
In this article, we’ll cover the key differences between APIs and microservices as answered by our contributors consisting of senior decision-makers and CTOs from technology companies around the world. One of the most popular ways to consume data from a web service is through a web application programming interface (API). By interface, we are referring to an agreement, or schema, that anyone using this API must abide by.
Your test management platform allows you to create test cases and assign them to testers. It makes it easy to analyze your results. But it doesn’t help you manage all of your testing resources in one place, and that’s a huge problem. In today’s world of online, remote work and the rise of the gig economy, distributed testing is not only widespread—but it also provides countless benefits to product and engineering teams (including faster releases).
Unless someone at your organization has built internal QA software, your in-house testers and your external testing resources are not testing under the same platform. We know, because no collective testing software exists, until now that is. We’ve enabled groundbreaking collective testing capabilities under our test management platform to keep your testing under one roof. But what does this really mean? And why should you—as a QA manager, dev team leader, or DevOps manager—care?
The success and growth of companies can be determined by the technologies they rely on in their tech stack. To deploy AI enabled applications to production, companies have discovered that they’ll need an army of developers, data engineers, DevOps practitioners and data scientists to manage Kubeflow — but do they really? Much of the complexity involved in delivering data intensive products to production comes from the workflow between different organizational and technology silos.
Well, it is International Fraud Awareness Week, focused on promoting fraud prevention and education. A fantastic initiative! Maybe I am naïve but I feel a bit sad that there is a need for “fraud week”. The insurance industry has a long and intimate relationship with fraud in many different ways. Insurance fraud can take place at a process or business function level, most notably in claims or underwriting.
