Operations | Monitoring | ITSM | DevOps | Cloud

The Splunk Deep Learning Toolkit (DLTK) is a very powerful tool that allows you to offload compute resources to external container environments. Additionally, you can use GPU or SPARK environments. In last Splunk blog post, The Power of Deep Learning Analytics and GPU Acceleration, you can learn more about building a GPU-based environment. Splunk DLTK supports Docker as well as Kubernetes and OpenShift as container environments.

One of our most exciting new projects at Splunk is coming to life. Over the past year, we have been hard at work putting together our vision: a place where Splunk admins, NOC/SOC teams, data analysts, and data scientists can collaborate, experiment, and operationalize their work, all in a single environment inside the Splunk ecosystem. We call it Splunk Machine Learning Environment (SMLE).

What is the usual criteria in choosing an online store? It should have reasonable prices, sell quality products, and most of all, it should have a fast loading time. A website’s performance is essential. A two-second delay can make a big difference to your website and revenue as well. In fact, Neil Patel reported that a mere second delay may cost an e-commerce site up to $2.5 million in sales annually.

“What’s that? You want me to send you a large video file or something? No, that’s not a problem, it’s actually easy, I’ll just stick it on Box. Or DropBox, or stick it on WeTransfer and then we’ll do a quick Zoom to go through it – what?

No matter the size, every company has to communicate with their customers – some need just to handle occasional questions and issues while others make and answer calls daily. But both can agree that customers’ expectations are increasing drastically, and living up to those expectations becomes more and more difficult.

Three years ago, when we released Embedded Views into beta, we were excited to enable customers to share log lines in a customized way outside of our web application. However, it's become clear that the vast majority of our users prefer having the full functionality of our web application when exploring their logs rather than using an Embedded View.

Most of the time, threat actors in the cybersecurity landscape don’t employ advanced techniques and tools to intrude and establish a foothold within networks. Often, they disguise malicious operations by mimicking the activities of legitimate users, leaving behind little to no footprint. Blending malicious actions with day-to-day IT activities helps attackers maintain a low profile and remain undetected for a longer period.

Azure IoT Edge is a Microsoft Azure service that allows you to run containerized workloads on IoT devices. With IoT Edge and Azure IoT Hub, Azure’s device-management platform, organizations across science, manufacturing, energy production, and other industries can provision their IoT devices and workloads at the edge of their cloud networks for immediate in-unit computing, a necessity when running AI algorithms or parsing large datasets directly on IoT devices.

All IT support teams know that problem management is used to identify the root causes which help to permanently resolve recurring incidents and follows specific steps like: However, problem management still remains an underrated, underutilized process which is mostly used together with incident or change management process. Problem management, out of all the ITSM processes has one of the lowest adoption rates.

Regulatory compliance is time-consuming and expensive. A recent survey of IT security professionals found that, on average, organizations must comply with 13 different regulations and spend an average of $3.5M annually on compliance activities, with audit-related activities consuming 232 person hours per year. With a team of five people, that adds up to 1.5 months a year devoted to audit-related activity. That’s a lot of hours that could have been spent on initiatives driving customer value.