In this tutorial i show how to use a GamePad within a React App. I render out all the buttons and their values and try out controlling styles of the app using the gamepad. The Code: https://github.com/danba340/react-gamepad Twitter: https://twitter.com/BarelyDaniel
Github: https://github.com/danba340 0:00 Intro
0:20 useGamepads hook
4:20 rendering Buttons
5:30 rendering Sticks
6:50 Labeling inputs
7:45 Controlling styles
9:30 Finishing words https://www.youtube.com/watch?v=PGVK5duPEjQ

Server-side WebAssembly has the potential to increase security, extend application portability, and simplify cloud-native applications when operated in the Kubernetes ecosystem. This talk explores the pros and cons of different deployment models – embedded in a container, native execution, or embedded into other components. We will demonstrate the power that WebAssembly brings to even those projects hosted in traditional containers. This talk features a live build, compilation, deployment, and operation of reference applications. Featuring wasmCloud, Krustlet, and Envoy. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=2OTyBxPyW7Q https://www.youtube.com/watch?v=JtwHtfFe6AI

The lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive on best practices, principles, patterns and techniques around production monitoring of machine learning models. We will cover standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models with Python leveraging advanced monitoring concepts such as concept drift, outlier detector and explainability. We'll dive into a hands on example, where we will train an image classification machine learning model from scratch using Tensorflow, deploy it, and introduce advanced monitoring components as architectural patterns with hands on examples. These monitoring techniques will include AI Explainers, Outlier Detectors, Concept Drift detectors and Adversarial Detectors. We will also be understanding high level architectural patterns that abstract these complex and advanced monitoring techniques into infrastructural components that will enable for scale, introducing the standardised interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=n0bR0IArJDo https://www.youtube.com/watch?v=04pzXTDrqv4