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The Good and the Bad of.NET Framework Programming

Altexsoft

NET is a software development framework and ecosystem designed and supported by Microsoft to allow for easy desktop and web application engineering. Since that time, the framework has undergone multiple iterations spanning runtime updates, new desktop graphical systems (WPF), APIs for service-oriented applications (WCF), and more.

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The Good and the Bad of Kubernetes Container Orchestration

Altexsoft

Clearly, there must be a mechanism to coordinate the work of such complex distributed systems, and that’s exactly what Kubernetes was designed for by Google back in 2014. It enables DevOps and site reliability engineer (SRE) teams to automate deployments, updates, and rollbacks. A ship loaded with containers, in our case.

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The Good and the Bad of Android App Development

Altexsoft

The popularity of an operating system in a given region is just one of many factors that go into deciding what platform to choose for your mobile app. But, when it comes to native development, you usually have to start with one. But, when it comes to native development, you usually have to start with one. Let’s begin.

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Letting change and uncertainty advance your software architecture

CircleCI

The market is changing, our business is evolving, our customer base is growing, and our team is scaling. How do we design our systems in a manner that can adapt and change to things that don’t even exist when we start building it? After that scaling phase, you’ll have a larger organization and far more teams.

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Must-Know Differences Between Git and SVN

Mentormate

In recent years, the use of Git has risen tremendously popularizing distributed version control systems. According to an Eclipse community survey, in 2014 Git finally surpassed SVN as a Version Control System (VCS) of choice for Java developers. Tools for Teams Considering PWAs. Centralized Version Control Systems.

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Generative Adversary Networks: A very exciting development in Artificial Intelligence

CTOvision

These observations make it important to discuss ways to optimize security of AI along with overall functionality of our systems. In fact, there is exciting, peer-reviewed research published on the topic and many interesting projects are well underway on methods to address some of these issues. These are just a few examples.

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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

There is often a need to verify the reasoning of such ML systems to hold algorithms accountable for the decisions predicted. Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g.,