GraphQL has replaced REST since its debut in 2015 and has gained popularity. It provides the flexibility frontend developers have longed for. The days of begging with backend developers for single-purpose endpoints are over. Now, a query can provide all the necessary data and request it at once, theoretically reducing latency by a significant amount. Everything was much easier with REST, especially monitoring.

It seems like everywhere you look these days you find a thinkpiece to grimly warn us of the dangers of artificial intelligence. But if you talk to anybody with an interest in the way technology interacts with human life – particularly in the world of service management – and instead of the grim pessimism of dystopian fiction, you’ll find sheer enthusiasm. There is tremendous potential for AI in ITSM.

As businesses grow and develop, so must the tools that help manage them. Application monitoring tools provide enterprises with a way to keep track of the health and performance of their applications and ensure that everything is running smoothly. Application monitoring tools have a wide range of capabilities and data that enterprises can use to help answer questions about the current state of an application.

In this blog, understand why your pod has OOMKilled errors when provisioning Kubernetes resources and how Speedscale can aid with automated testing. When creating production-level applications, enterprises want to ensure the high availability of services. This often results in a lengthy development process that requires extensive testing for the applications or a new release.

You’ve probably heard about neural networks being hailed as the next big step in technological advancements in artificial intelligence (AI). Beyond its often exaggerated depiction in fiction and media, neural networks have slowly but steadily become an invaluable asset in the IT world. It is under constant research in data science and computer science.

Running machine learning (ML) inference in Edge devices close to where the data is generated offers several important advantages over running inference remotely in the cloud. These include real-time processing, lower cost, the ability to work without connectivity and with increased privacy.

The Machine Learning team at Splunk has been hard at work over the last several months preparing for a few exciting launches at.conf22, held just a few weeks ago. Splunk customers want to leverage machine learning (ML) in their environments, but many aren’t sure how to use it, or even how to get started.