Workload Automation Blog

Workload automation and job scheduling have become critical as application and IT processing requirements have expanded. Automation for workloads across a broad spectrum of operating systems, applications, databases, and dependencies is now fundamental to on-time service delivery. Learn about Workload Automation at BMC or explore BMC's Introduction to Hadoop.

Using GPUs (Graphical Processing Units) for Machine Learning

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You are probably familiar with Nvidia as they have been developing graphics chips for laptops and desktops for many years now. But the company has found a new application for its graphic processing units (GPUs): machine learning. It is called CUDA. Nvidia says: "CUDA® is a parallel computing platform and programming model invented by NVIDIA. It … [Read more...]

Top 17 DevOps Conferences of 2018

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Whether you are looking to stay on top of emerging trends, hear from some of the top experts in the field, or network with other industry professionals, attending a DevOps conference is an excellent place to start. This list, freshly updated for 2018, will help you choose which conferences to put on your schedule. Each year, there are numerous … [Read more...]

IT Orchestration vs Automation: What’s the Difference?

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Automation is a buzzy topic in today’s IT world. Whether you work in IT or in another area of the business, you may hear about automating as a way of saving money, improving efficiencies, and removing inherent errors. Often, automation is a term that may be used only partially correctly. In this article, we’re clearing up the differences between … [Read more...]

DevOps in the Cloud

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In today’s fast-paced, digital-first business environment, DevOps is rapidly becoming as mainstream as the Cloud. Yet many companies are still grappling with questions around how to implement, foster and benefit from a DevOps culture – particularly in cloud environments. This year, I’ve had the opportunity to attend DevOps events around the world … [Read more...]

Introduction to Neural Networks Part II

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In the previous post we introduced the concept of perceptrons, which take inputs from simple linear equations and output 1 (true) or 0 (false). They are the left-hand side of the neural network. But as Michael Nielsen explains, in his book, perceptrons are not suitable for tasks like image recognition because small changes to the weights and … [Read more...]