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.

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.

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.

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.

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.

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!

Unless you build or program computers for a living, you could be forgiven for never having wondered what actually happens under the hood. How do computers make decisions? What do their instructions look like? Can they, y’know… think?

The hype has gone off the charts for AI and machine learning tools' potential in IT organizations. It’s time to temper expectations and move towards a sensible adoption path. Artificial intelligence is not fairy dust sprinkled on a data center, despite the enthusiastic proclamations of many IT operations vendors today. A couple of years ago, AI was the bride atop the wedding cake. It was perfect, with promises to render obsolete errors and out-of-control performance issues.

The difference between machine learning for business and machine learning for consumers is that for business applications, you must deal with requirements that casual users won’t encounter.