Vulnerability Assessment on Azure Container Registry with Microsoft Defender and Docker Hub
Azure Container Registry: Microsoft Defender for Containers and Docker Scout. Rather than comparing these tools, I want to highlight how they complement each other to enhance container security on ACR. Containers revolutionize the way applications are deployed, providing a consistent and efficient way to package code and dependencies. However, with this convenience comes the critical need for robust security measures. In this post, we'll explore why container security is essential, focusing on preventing malicious code, avoiding vulnerabilities, and managing configuration and deployment risks.Host and Deploy Images on Azure Container Registries (ACR) via App Service - A step-by-step guide
Ready to unlock the full potential of Azure for your web applications? Join me, Suzaril Shah, as I take you through a comprehensive guide on hosting images in Azure Container Registry and deploying them through an App Service plan. Whether you’re a seasoned developer or new to cloud services, this step-by-step tutorial will equip you with the skills to streamline your deployments and enhance your project’s infrastructure. Don’t miss out—let's elevate your Azure proficiency together!Deploying Flask Apps to Azure Web App via Docker Hub
Learn the technical intricacies of deploying Python-based Flask apps to Azure Web App using Docker Hub. Follow the step-by-step guide, covering Flask development, Docker containerization, and Azure infrastructure setup with Terraform. Gain insights into Azure CLI installation and Web App deployment, ensuring optimal performance. Explore continuous deployment options, allowing automatic redeployment upon Docker image updates. Master the seamless integration of technologies for a live and accessible book recommendation system, 'BookBuddy,' while optimizing for Azure's robust features.Dev Database to go! The perfect database for developer
When building a new project, we don't need a big database that scales and has lots of data, but we do still need some kind of data source. Of course, it is possible to fake it and have some hardcoded value returned by an API but that takes time to create and it's not a database. In this post, I want to share a solution to have a portable, self-healing, disposable, disconnected database that doesn't require any installation.Get started with Docker and Containers.
Docker solves a lot of problems like: - Creating a reproduceable environment, great if you want Dev, Staging and Pro environment to look the same. - Helps with onboarding. Onboarding developers used to take weeks, before they had installed all the programs, all the libraries, configured everything correctly. - Works on my machine AND in the Cloud. The Docker container is a black box that runs the same everywhere because you specify what's in there from, variables, libs, your app, and hosting layer. - Microservice architecture, a lot of system today are split up into many small pieces APIs. To build all of these small pieces you need a container technology like Docker. As a new developer, learning about using containers is a must as so many Companies use it and it's used evreywhere from Web, to ML to IoT. Here's a collection of tutorials that takes you from the very beginning.Using Visual Studio Code from a docker image locally or remotely via VS Online
Adevelopment containeris a runningDockercontainer with a well-defined tool/runtime stack and its prerequisites. The Remote - Containers extension in theRemote Developmentextension pack allows you to open any folder mounted into or inside a dev container and take advantage of VS Code's full development feature set.High Performance Real time object detection on Nvidia Jetson TX2.
Real time object detectionon myNvidia Jetson TX2.Thereal timeterm here simply means, low latency and high throughput. It's a very loosely defined term, but it's used here in contrast to the store-and-process pattern, where storage is used as an interim stage.Bringing R Workloads to Azure Machine Learning Service
The two of the most popular platforms for data science workloads are – R and Python. Both platforms have a large ecosystem of libraries and support which enable developers and innovators to build solutions quickly and efficiently. Looking more closely however, it is quite evident that around the academic and research, R draws at least as many supporters as Python.https://twitter.com/jplaneUnderstanding Containers - the layer between the operating system’s kernel and the application
First published on MSDN on Jun 22, 2018 Guest blog by Christo Lolov, Microsoft Student Partner at Imperial College LondonAbout me I am a Third Year, Computing MEng at Imperial College London and a have a number of blogs already published on various areas of technology which interest me see https://blogs.
