Operations | Monitoring | ITSM | DevOps | Cloud

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we discuss the escalating significance of AI and machine learning in IT security space.

In this guest article John Arundel, consultant and author of Cloud Native DevOps with Kubernetes, introduces the Checkly Terraform provider. He explains what Terraform is and why you'd want to use it, and shows how to use Terraform to automate and manage your website monitoring with Checkly.

Industry 4.0 technology is transforming industry all over the world. Digital innovations such as data, virtual reality, automation, and robotics are all helping brands create products and services and deliver them to customers in better and more efficient ways than ever before. One of the most useful elements of Industry 4.0 technology comes from the Internet of Things.

This article originally appeared in TechBeacon. Gartner first coined the term "AIOps" a few years ago to describe "artificial intelligence for IT operations," and over the last few years, IT operations monitoring tool vendors have begun incorporating AIOps features into their products. Now AIOps tools are commonplace, but many IT leaders remain cautious about using these relatively new capabilities.

The Deep Learning Toolkit (DLTK) was launched at .conf19 with the intention of helping customers leverage additional Deep Learning frameworks as part of their machine learning workflows. The app ships with four separate containers: Tensorflow 2.0 - CPU, Tensorflow 2.0 GPU, Pytorch and SpaCy. All of the containers provide a base install of Jupyter Lab & Tensorboard to help customers develop and create neural nets or custom algorithms.

Ever walk into a corner market, push on the door and find it won’t open? You look down at the handle and are reminded by a sign on the door that you have to “pull” to open it? The LogicMonitor platform uses an agentless collector to pull metrics from thousands of devices and resources into a unified monitoring view (no agents required). We currently offer more than 2,000 LogicModules out-of-the-box that gather metrics from all kinds of systems using many different protocols.

Though much of the coverage of artificial intelligence or AI has been hype, the technology itself is real enough – and gaining traction in the commercial sphere. In fact, AI is increasingly being viewed as an integral requirement for business IT setups, rather than a luxury or fad. The research firm Gartner, Inc., predicts that more than 30 percent of data centers that fail to sufficiently prepare for AI will no longer be operationally or economically viable by 2020.

In the era of millennials and Gen Z, there's an app for everything. In fact, Apple trademarked the sentence, "There's an app for that," nearly 10 years ago. Today, with more than 2.47 million apps available across the App Store and Google Play Store, they're not only a convenient and affordable means to access a service; they have become extensions and expressions of ourselves and our personalities.

If you’re a tech geek you must have come across disrupting technologies like Artificial Intelligence, Machine Learning, Big Data, and IoT. These are the key buzz words since many years. With this blog we plan on kickstarting 2020 with the most sought-after question amongst IT administrators & DevOps team, “How will AI & ML benefit us?”, “What’s the role of AI in Networks?” & more. Well, let’s get started!

Traditional enterprise application platforms are usually built with Java Enterprise technologies and this is the case as well for OpsRamp. However, in machine learning (ML) world, Python is the most commonly used language, with Java rarely used. To develop ML components within enterprise platforms, such as the AIOps capabilities in OpsRamp, we have to run ML components as Python microservices and they communicate with Java microservices in the platform.