AI and Machine Learning are becoming critical differentiators in the technology landscape. By their nature, AI and ML are computation hungry workloads. They require best-in-class distributed computing environments to thrive. AI and ML present a perfect use case for Kubernetes, the distributed computing platform engineered at Google to run their massive workloads.

Earlier this year, I predicted that Artificial intelligence (AI) would move from innovation labs to mainstream workflow projects this year. In IT organizations, I believe service management teams will lead this trend. Here’s why.

Beginning with huge systems occupying large physical space, technology has enabled smaller, versatile devices that can be used anywhere at anytime. From the communications and hospitality industries to the public sector, either on-premises, cloud, or hybrid services were employed to make processes cost-effective.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore the concept of XAI, Explainable AI.

Today, the number of possible failure modes in cloud and microservices applications are exploding, making it increasingly difficult to gain true observability and take the right action across IT environments. According to Lightstep’s Global Microservices Trends report, 91% of teams are using or have plans to use microservices, but 73% report it is harder to troubleshoot application performance problems due to greater complexity.

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.