“What would it be like to have an asset monitoring solution that can be installed within minutes and is independent of all existing IT systems, without endangering existing processes?” LineMetrics was founded in 2012 in Haag, Austria, in response to questions just like this one. LineMetrics developed a complete real-time asset monitoring solution delivered through its end-to-end Internet of Things (IoT) platform.

In sync with the upcoming release of Splunk’s Machine Learning Toolkit 5.2, we have launched a new release of the Deep Learning Toolkit for Splunk (DLTK) along with a brand new “golden” container image. This includes a few new and exciting algorithm examples which I will cover in part 2 of this blog post series.

In part 1 of this release blog series we introduced the latest version of the Deep Learning Toolkit 3.1 which enables you to connect to Kubernetes and OpenShift. On top of that a brand new “golden image” is available on docker hub to support even more interesting algorithms from the world of machine learning and deep learning! Over the past few months, our customers’ data scientists have asked for various new algorithms and use cases they wanted to tackle with DLTK.

Here is an overview of the features & fixes we released over the last month.

TL;DR: yes, API Gateway can replace what a Load Balancer would usually provide, with a simpler interface and many more features on top of it. The downside is that it doesn’t come cheap. Load balancers have been one of the most common ways to expose a backend API to the public or even to an internal/private audience. API Gateways seem to provide the same functionality: map and connect HTTP requests to a backend service. So, are they the same or are there any differences?

As we connect with customers we increasingly hear the need for teams to be more predictive with their data. A big challenge is uncertainty around how to get started, especially when much of their data is unstructured. At Splunk, our goal is to make data — and machine learning — accessible for a broad range of users. The good news is, with machine learning doing even more work on your behalf, you don’t need to be a data scientist to use these advanced capabilities.

Business leaders are always looking for ways to leverage technology so that employees can do their best work and meet evolving customer demands. While we settle into the new normal of remote work, mobile technology is taking on a new role in the enterprise, as employees adapt to an increased blending of their professional and personal lives. At ServiceNow, we recently conducted new research to understand how employees are taking advantage of mobile services offered by their organizations.

We are thrilled to announce that we have just raised a $2.25M seed round. This round is led by Accel and some great founders — including current and former executives from Vercel (formerly Zeit), Instana, Twilio and many more. Welcome, on board! Active monitoring and end-to-end testing have been silos with different tools and platforms. Modern DevOps demands a change. We firmly believe that Checkly sits at the intersection of these distinct market trends.

We just closed a $2.25M investment round led by Accel and a pack of top rate Angels. Lots to talk about, here's the itinerary: Onwards! Let's start at the end. Three months ago Checkly was a bootstrapped solo project run by me from a desk in the back of a Berlin fashion label's spare floor space (Frisur!). Now we are six people on a mission to give active monitoring and E2E testing a very serious kick in the butt. Here is a copy & paste from our pitch deck.