In this four-part series, you’ll learn how to create an Intelligent App with Azure Container Apps. In this fourth and final part, you’ll explore how to integrate a custom model into your Intelligent Apps, enhancing the application’s features with specialized AI.
In the final part of our series, we’ll dive into how to harness AKS’ powerful features like auto-scaling and high availability to manage variable workloads and maintain continuous service.
In topic 2 of our series, we explore the power of multi-model databases for Intelligent Apps and their integration with Azure Cosmos DB and Azure Kubernetes Service (AKS).
In the last article, we created an Intelligent App that leverages Azure AI Vision to analyze images and extract data. We developed an API to perform optical character recognition (OCR) on uploaded images and tested this API locally.
In this first article of our hands-on series, we’ll delve into creating an Intelligent App that leverages Azure AI Vision to analyze images and extract data.
In this article, you will build a Qt-based Python application with two C/C++-based DLL dependencies. This architecture mimics a typical scenario of using Python and Qt for rapid UI prototyping and DLLs for computation-intense work.
Classic globbing and modern gitignore-style globbing algorithms can be fast, whereas recursive implementations are known to blow up exponentially; why some freely available source code should not be used.
This article demonstrates the convenience of using native Arm Python 3.11 on Arm-powered devices to experience up to a threefold performance boost over using it in emulation mode.
This article describes how to divide two polynomials and shows the source code to calculate this division. There is also code to add, subtract and multiply two polynomials.
In this article, I will discuss what I experienced while creating a custom model for the detection of backyard pests. You can download the resulting critters.pt file.
This tutorial will go through the GeoPandas GeoDataFrame, quick and easy visualization of the OSM street networks, with additional helper functions for added customization of the resulting maps using OSMnx, with code samples.
In this article, we’ll wrap the face identification model in a simple Web API, create a client application on the Raspberry Pi, and run the client-server system.
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