OverOps has joined the Cloud Native Computing Foundation (CNCF), reinforcing our commitment to support our customers and partners on their cloud native journeys. We’re excited to announce that OverOps has joined the Cloud Native Computing Foundation (CNCF)! CNCF is part of the nonprofit Linux Foundation and is the vendor-neutral home for many of the fastest-growing open source projects, including Kubernetes, Prometheus, and Envoy.

You may not know it, but you’ve probably used microapps on your computer or mobile device. They’re single-purpose apps. They don’t seem to do much, but they excel at performing specific tasks.

In part 1 we talked about the industrial applications and benefits that 5G and fast compute at the edge will bring to AI products. In part 2 we went deeper into how you can benefit from this new opportunity. In part 3 we focused on the key technical barriers that 5G and Edge compute remove for AI applications. In this part we will summarise the IoT use cases that can benefit from smart cell towers and how they will help businesses focus their efforts on their key differentiating advantage.

In part 1 we talked about the industrial applications and benefits that 5G and fast compute at the edge will bring to AI products. In part 2 we went deeper into how you can benefit from this new opportunity. In this part we will focus on the key technical barriers that 5G and Edge compute remove for AI applications.

In part 1 we talked about the industrial applications and benefits that 5G and fast compute at the edge will bring to AI products. In this part we will go deeper into how you can benefit from this new opportunity.

Shipping the right app with the right functionalities to the right customer, at the right time is how you boost your ranking in the app store. Some of the key steps in this process are: continuously releasing high-quality software, listening to feedback, and providing new features to users — before they even know that they want them. To achieve all of this, app developers need two main things: automation tools and a growth mindset.

With Elasticsearch machine learning one can build regression and classification models for data analysis and inference. Accurate prediction models are often too complex to understand simply by looking at their definition. Using feature importance, introduced in Elastic Stack 7.6, we can now interpret and validate such models.