From Continous Integration to Continuous Delivery

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Eberhard Wolff

Shows how Continuous Integration is extended by Continuous Delivery. Provides an introduction to both.

Technologie

From Continous Integration to Continuous Delivery

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Many organizations have already taken the leap into DevOps. Luckily, this daunting path is now well lit with best practices from customer experience, toolkits for success, and warning signs for ugly DevOps practices. Guest host Amy DeMartine, Senior analyst at Forrester Research, and Perforce invite you to a live broadcast on using DevOps to break your team's bad habits and increase your business value with speed, minimal errors, and pro knowledge on new ways to collaborate. In this broadcast, you'll learn to: - Use Agile and DevOps to improve collaboration and simplify delivery - Avoid bad DevOps habits - Build a toolkit for success and embrace uncertainty - Reference a use case from one of Perforce's largest customers when setting up your own release cycles

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Author: Oleg Chunikhin, www.eastbanctech.com Kubernetes is a portable open source system for managing and orchestrating containerized cluster applications. Kubernetes solves a number of DevOps related problems out of the box in a simple and unified way – rolling updates and update rollback, canary deployment and other complicated deployment scenarios, scaling, load balancing, service discovery, logging, monitoring, persistent storage management, and much more. You will learn how in less than 30 minutes a reliable self-healing production-ready Kubernetes cluster may be deployed on AWS and used to host and operate multiple environments and applications.

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CONTINUOUS DELIVERY REFERENCE ARCHITECTURES Including Sonatype Nexus and other popular DevOps tools Derek E. Weeks (@weekstweets) VP and DevOps Advocate Sonatype. Continuous Delivery and DevOps Reference Architectures include many common tool choices. The most common tool choices we find in these reference architectures are: Eclipse, git, Cloudbees Jenkins / Atlassian Bamboo, Sonatype Nexus, Atlassian JIRA, SonarQube, Puppet, Chef, Rundeck, Maven / Ant / Gradle, Subversion (svn), Junit, LiveRebel, ServiceNow

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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