We're very happy to introduce groups to Checkly! Grouping checks opens up a range of possibilities and workflow enhancements of which this initial release is just the tip of the iceberg. Here are some the thing you can start doing right now.

In the first part of this blog article, I introduced key concepts surrounding data ingestion for the industrial Internet of Things, the role and importance of metrics and self-services capabilities for shop floor personnel. So let's see how this looks in practice and how the knowledge of a process or control engineer can be turned into action.

This blog originally appeared on HPE. Hewlett-Packard Enterprise (HPE) recently invested in OpsRamp. Consider this: when you drive a car, how would you know whether the car needs maintenance? You might look for status indicators like a check engine or change oil light. Similarly, when a pilot flies an airplane, they rely on a multitude of metrics and data to ensure the plane is running smoothly. Managing IT infrastructure is similar in nature.

Using dashboards in Stackdriver Cloud Monitoring makes it easy to track critical metrics across time. Dashboards can, for example, provide visualizations to help debug high latency in your application or track key metrics for your applications. Creating dashboards by hand in the Monitoring UI can be a time-consuming process, which may require many iterations. Once dashboards are created, you can save time by using them in multiple Workspaces within your organization.

Imagine that your API response includes data that you would like to track and get regular reports on, and you may even want to generate alerts when values hit certain thresholds. You’re already monitoring for API performance and function, but how about grabbing the other data at the same time. Wouldn’t it be nice to capture the data, evaluate it, and store it in your Uptrends account? You can with Uptrends’ newest Multi-Step API feature, Custom Metrics.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. Most companies use some type of “as-a-service” solution to optimize output, and this week, we shed some light on the latest “as-a-service” trend to hit the market, AI-as-a-Service.

We’re proud to announce the availability of the fresh, new “internal API” to our users. This API is only accessible from within a running instance on the platform.

In the first part of this blog series, we presented a use case on how machine learning can help to improve police operations. The use case demonstrates how operational planning can be optimized by means of machine learning techniques using a crime dataset of Chicago. However, this isn’t the only way to predict and prevent crime. Our next example takes us to London to have a look at what NCCGroup’s Paul McDonough and Shashank Raina have worked on.