These little ones, are extremelly funny, and they literally don't care about the cold . So, I decided to help them and build an Automatic Feeder using Azure IoT, a Wio Terminal and maybe some more devices. You can check the Azure IoT project here Azure IoT - Squirrel Feeder. Once the feeder was ready, I decided to add a new feature to the scenario, detecting when a squirrel is nearby the feeder. Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own image identifier models.

This article is the final article in the 3-part series for Image Recognition in WhatsApp Chatbot. The first article, LUIS – Create a Conversation App discussed more on creating a service in Azure for LUIS. The second article Image Recognition in WhatsApp Chatbot - Using Azure AI continued on to create models and Image Recognition service on Visual Studio. This last article focuses on using Twilio and Azure function app to develop the WhatsApp Chatbot. Twilio offers the service of cloud communication platform (CPaaS) to enable developers to make and receive phone calls programmatically, send and receive messages in text format as well as perform numerous other communication functionalities through web service APIs.

Real-time application scenarios such as chat for streaming videos, interactive whiteboards for remote education, and IoT dashboards are becoming ever more popular. Businesses are keen to build such applications for enhanced user experiences and real-time interactions with end customers. Today, we are announcing the preview of the Azure Web PubSub service for building real-time web applications with WebSockets. WebSocket is a standardized protocol that provides full-duplex communication. It is key to building efficient real-time web interactions and is supported by all major browsers as well as web servers.

I remember the first time I created a simple machine learning model. It was a model that could predict your salary according to your years of experience. And after making it, I was curious about how I could deploy it into production. If you have been learning machine learning, you might have seen this challenge in online tutorials or books. You can find the source code here if you are interested.

Recent advances in deep learning and cloud-based infrastructure have led to innovations in models for various domains like natural language processing, computer vision, recommendations. Of course, developing the model is only half the story. Your models are mostly useful once they are served up for making predictions for consumption in in AI-driven scenarios from the end applications. It is important to do it in a cost-effective and reliable manner. However, managing infrastructure for hosting your models is challenging as it involves several aspects like maintaining your fleet, ensuring reliability, scaling, security and ongoing monitoring and management.

Sketch2Code is a solution that uses AI to transform a handwritten user interface design from a picture to valid HTML markup code. The training set used to create the sample model used in the project is located in the Model folder. Each training image has a unique identifier that matches information contained in the dataset.json This file contains all the tag information used to train the sample model. To create your own model you can use this dataset to start and using the Custom Vision API upload this dataset to your own project.

With the release of ML.NET, a API that C# developers can use to infuse their applications with machine learning capability, I've been keen to combine my knowledge of Azure Functions with the API to build some wacky serverless machine learning applications that would allow me to enhance my GitHub profile and cater to all the buzzword enthusiasts out there! This post won't be a tutorial. I'm writing this more as a retrospective of the design decisions I took while building the application and the things I learnt about how different components work. Should you read this and decide to build upon it for your real world applications, hopefully you can apply what I've learnt in your projects or better yet, expand on the ideas and scenarios I was working with. I'll be focusing more on what I learnt about the ML.NET API itself rather than spending too much time about how Azure Functions work.

Functional programming in pure form implies no state and no side effects when called (since there is no state). Azure Functions (much like AWS Lambda and Google Cloud Functions) is a neat concept where you don't need any explicit infra, you just deploy a function and reference it via an endpoint (URI). During my search for the ultimate (aka, ultimately cheap/free) deployment for my ML models, I thought I would try this out since the pricing docs state a high free # of executions. Setup is easy, go to Azure Portal and run thru the usual "Create" and search for "Functions" Not well documented is that you have to install both the node package (npm install -g azure-functions-core-tools) and the python package via pip (pip install azure-functions) if you are coding in Python on Visual Studio Code. Some languages have an online editor within Azure Portal which is kind of nice (though kind of dangerous).

This article is part of #ServerlessSeptember. You'll find other helpful articles, detailed tutorials, and videos in this all-things-Serverless content collection. New articles are published every day -- that's right, every day -- from community members and cloud advocates in the month of September. Azure Functions recently announced the general availability of their Python language support. We can use Python 3.6 and Python's large ecosystem of packages, such as TensorFlow, to build serverless functions. Today, we'll look at how we can use TensorFlow with Python Azure Functions to perform large-scale machine learning inference.

Microsoft Cloud Workshop (MCW) is a hands-on community development experience. Microsoft Cloud Workshop (MCW) is a hands-on community development experience. Use the following filters to search our database of Microsoft Cloud Workshop materials. Each workshop includes presentation decks, trainer and student guides, and hands-on lab guides. Design a modernization plan to move services from on-premises to the cloud by leveraging cloud, web, and mobile services, secured by Azure Active Directory.