DevOps technologies enable the fast and efficient implementation of new features in a complex cloud architecture. This increases both the speed and quality of productive deployments and thus generates ever greater added value for your cloud product and your customers.
DevOps technologies enable the fast and efficient implementation of new features in a complex cloud architecture. This increases both the speed and quality of productive deployments and thus generates ever greater added value for your cloud product and your customers.
DevOps technologies enable the fast and efficient implementation of new features in a complex cloud architecture. This increases both the speed and quality of productive deployments and thus generates ever greater added value for your cloud product and your customers.
The core principles of DevOps include close collaboration between development/operations teams and use of unified software solutions to automate various manual tasks. The typical work cycle in the DevOps model consists of the following phases: Plan, Implement, Test, Deploy, Operate, Monitor, Feedback. The individual phases form a highly iterative flow over time in order to fulfill emerging customer requirements as quickly as possible. Translated with www.DeepL.com/Translator (free version)
Use existing know-how in combination with cloud-native services from Azure, AWS and GCP.
Systematic recording and planning of tasks with Scrum and Kanban boards.
Code versioning for both the product and the underlying infrastructure.
Automated deployment and testing through continuous integration and continuous delivery (CI/CD) pipelines.
Saving build artifacts in a cloud-native artifacts repository or container image registry.
Dedicated microservices and container services for modern distributed applications.
Unlimited memory and compute resources for upscalability and flexibility.
A Deep Dive Case Study into Methodologies and the RAGAS Library Stock image, AI generated. In our previous article, we explored why...
Why does it matter? Stock image, AI generated. Large language models (LLMs) have moved beyond experimental phases to become mission-crit...
A recent project required us to map a large number of GPS coordinates to their respective municipality names. This process, known as reverse ...
Introduction SageMaker model training. Training machine learning models on a local machine in a notebook is a common task among data sci...
You will shortly receive an email to activate your account.