#Docker rapidminer studio software#
Current software development endeavors may combine several programming languages such as Java, Javascript, HTML or SQL with different deployment strategies such as Docker, Kubernetes, AWS, or Google Cloud. Software development is a knowledge-intensive and collaborative activity, facing a constant challenge from the almost continuous change in the underlying technology. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. Specifically, Q&A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). Learn to operate your deployment by reading our technology overview.Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects.If you need to fine-tune parts of the deployment, visit our docker image reference for detailed configuration possibilities.Our templates are designed to work out of the box. This allows you to select a template which is closest to your needs. Then, check out our docker-compose templates or kubernetes templates, depending on your use-case.
Please also read our security overview page. Here you'll learn the components used in the platform and their role. First, take the time to familiarize yourself with a high level overview of the platform deployment architecture.If you would like to deploy with a specific use-case in mind, and the cloud images are not suitable for your needs, here is the reading order we recommend. We recommend this deployment method mainly for development and testing purposes. The quickest and easiest way to deploy RapidMiner AI Hub is via our cloud images, which are available on AWS and Microsoft Azure. These images can be used to deploy the platform using the Docker-compose or Kubernetes orchestration technologies, either on-prem, or in your preferred (public or private) cloud infrastructure. To avoid this task, we have packaged the components as a set of docker images.
#Docker rapidminer studio manual#
Although manual installation and configuration of these components is possible, it may prove challenging and time-consuming, especially in a distributed environment. RapidMiner AI Hub has adopted a deployment architecture consisting of several components, to ensure scalability and flexibility. To learn about Docker deployment, continue reading. The legacy RapidMiner Server documentation is still available,īut to deploy RapidMiner AI Hub, we recommend using Docker.
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