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.

As online commerce has boomed, many companies now manage a large number of revenue streams from a variety of sources including micro-transactions, single purchases, and subscription plans. Now that revenue models have become much more complex and fragmented, many companies have realized that their traditional systems simply aren’t capable of the scale and granularity required for accurate revenue monitoring.

Binary classification aims to separate elements of a given dataset into two groups on the basis of some learned classification rule. It has extensive applications from security analytics, fraud detection, malware identification, and much more. Being a supervised machine learning method, binary classification relies on the presence of labeled training data that can be used as examples from which a model can learn what separates the classes.

As COVID-19 continues to make clear, being adaptable and resilient when the world changes can help a business stay alive. At Elastic, we know from experience that being distributed helps build a strong company that can scale and adapt as new challenges arise. In the spirit of open source and our relationship with the Elastic community, we’ve been offering tips and tricks on our blog and on social media about how to work effectively while remote.

Managing a network more effectively has been something our customers have been asking us about for many years, but it has become an increasingly important topic as working from home becomes the new normal across the globe. In this blog series, I thought I’d present a few analytical techniques that we have seen our customers deploy on their network data to: Better understand their network and Develop baselines for network behaviour and detect anomalies.

A difficult question we come across with many customers is ‘what does normal look like for my network?’. There are many reasons why monitoring for changes in network behaviour is important, with some great examples in this article - such as flagging potential security risks or predicting potential outages.