How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.
Java 10 was “the fastest in Java’s 23-year history” and now we’re just one month away from Java 11 so it’s not far-fetched to say that Java’s new chapter is exciting. This issue depicts Java’s new journey but it’s not (just) an ode to this 23-year-old programing language; if you’re a DevOps aficionado or expert, you’ll surely enjoy the second part.
The rapid evolution of cloud development technologies is enabling enterprises to deploy services more reliably, scale them, and change them more rapidly. At the same time, failure to keep up with this evolution can rapidly lead to ending up with a brittle cloud implementation. In this article, John Mathon talks about the transformational cloud technology developments enterprises cannot afford to ignore.
There are a number of issues that arise when data scientists and ML researchers meet DevOps to try to deploy, audit, and maintain state-of-the-art AI models in a production and commercial environment. Peter Zhokhov and Arshak Navruzyan discuss a new open source software tool called Studio.ML, which offers a number of solutions to this problem.
DevOps’ full potential can only be achieved if development and operations work together with the specialist departments and management on a value adding product. Instead of DevOps, it should be called BizDevOps. Sebastian Schulze and Jacob Tiedemann explain how the integration of business and DevOps changes the cooperation, organization, and processes of companies.
Companies are deploying cloud-native applications to meet their business requirements. Whether it is speed, scalability or new functionality, containers offer a route to achieving these goals faster and more efficiently to manage the cost of change for developers and operational stakeholders. However, are we aware of the challenges as well?
From the principle of Getting Things Done, to DevOps and agile practices, automation is the number one word in everyone’s mouth. Here we take a look at the most interesting results of The Pervasive Automation Report by Puppet and how the picture of automation adoption among enterprises looks like.
In the world of DevOps, traditional application security is no longer enough. How can we improve AppSec? What are the newest security challenges that arise as DevOps becomes more mature? JAXenter editor Gabriela Motroc caught up with Tim Mackey, technical evangelist for Black Duck by Synopsys at DevOpsCon 2018 to talk about all this and more.
Jira is most probably the number one choice software for agile development. And with all the good reasons. But no one is perfect and neither is Jira. Its fatal flaw? Pricing! Let’s have a look at some worthy and, most importantly, less costly alternatives to Jira.
How many of you are familiar with terms like test, value or behavior-driven development? In this talk, Helen Beal goes over these terms and dives into DevOps-driven development and delivery.
As the business value of DevOps has come around as quite profound an impact, DevOps consulting companies have gained momentum. In this article, Ajeet Singh goes through the challenges you face without DevOps.
How do you choose the most important metrics? Co-founder and Principal Consultant at Lagom Solutions, Julia Wester spoke at DecOpsCon 2018 about “Finding metrics that matter and using them safely”. In this interview, we discuss with her the importance of proper metrics when utilizing DevOps.
Change is never easy. More and more companies, however, are interested in adopting the DevOps model. In this article, Rami Sass, CEO and co-founder of WhiteSource puts together a cheat sheet of tools and practices that can help your team become a lean, mean, DevOps machine.
It is time for all of us to live the DevOps dream! GitLab 11.0 is here and it brings with it the essence of what DevOps strives to achieve the most: automation!