Open AQAvit Test Strategy for OpenJDK binaries are based on open, transparent, robust, and adaptable test suites. We are establishing AQA as the comprehensive open quality standard for OpenJDK verification. AQAvit verification demonstrates that the product is a high-quality offering, ready for enterprise usage, and production quality inclusion criteria. It is one of the three requirements for listing at the Adoptium Marketplace.
Moving AI models from development to production has its own set of challenges and risks when the Edge is involved.
In this laboratory we're going to learn how to overcome these challenges leveraging the Nvidia Triton™ Inference Server, Eclipse Kura and Eclipse Kapua, two frameworks for the Edge and Cloud.
This laboratory will give attendees a hands-on experience of the entire lifecycle of an Edge AI application, from data collection to training to model deployment.
The objective of this presentation is to introduce and demonstrate the Zenoh-Flow framework as a solution to the challenges of developing and managing machine learning (ML) applications in the decentralized Cloud-to-Things continuum.
The presentation will highlight the following points:
The applications of real-time machine learning on human mobility data continue to grow, thanks to the increase in digital mobility data, such as phone records, GPS traces, and social media posts. This has led to exponential growth in areas such as next location prediction, crowd flow, trajectory generation, flow generation, disease spreading, urban projection, and well-being.
With the Cloud buzz all around us, whether deploying microservices or traditional applications, scaling applications, going serverless and more, there are challenges to be solved at all layers (Hardware, Container, Runtime, App Server, Application). And it is important to tune each layer to achieve improved performance with cost benefits
In this talk, we will walk you through:
Future-proof your Testing with Machine Learning Drive innovation by empowering your teams with end-to-end tests that self-heal and run at-scale in the cloud. Speed up development, lower costs, and improve quality in CI/CD environments. Avoid wasting time fixing broken test cycle. You can dynamically update your tests using machine learning to keep up with product changes. Quickly diagnose test failures with suggestions. Also achieve adaptive execution at scale by running as many test cycles as often needed.
Artificial intelligence (AI) software and hardware advancements are resulting in a slew of smart gadgets that can detect and react to sights, sounds, and other patterns without the need for a constant internet connection.
Nowadays, researches are looking for adaptation of Machine Learning algorithms to testing processes to reduce the manual effort and improve quality. In this talk, we will discuss in detail Machine Learning practices with a case study. Testing efforts are potentially able to be minimized in all stages.
I aim to demonstrate how ML helps in all stages. In this manner, I summarize the application areas with algorithms and discuss the advantages and potential risks of AI applications in software testing.
- MNIST
- Autopilot (NVIDIA)
- Emojinator
- Malaria Detection
- Quick, Draw (Google)
So to pinpoint the techniques for minimization of CPU resources, I am going to discuss
