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
Code formatting is an opinionated beast. It always has been a matter of taste, and it always will be a matter of taste. This is the reason, why professional formatting tools, such as Eclipse JDT, offer a gazillion number of options. Which is still not sufficient enough. After all, you can override them inline with tag-comments to make the formatter shut up. Can't we do better than that? What if we could use machine learning techniques to detect the preferred code style that was used in a codebase so far? Turned out, we can.
Teaser: Today, the development of automotive software functions based on artificial intelligence still needs a lot of different tools, languages and frameworks.
In this talk, we introduce how we built upon the Eclipse eco-system an integrated development environment called "DAISY" (Development environment for Artificial Intelligence SYstems).
