Welcome to an illustrated guide to Azure Percept – a new end-to-end edge AI platform from Microsoft that helps IoT practitioners go seamlessly from silicon to services when developing & deploying intelligent edge applications. The guide gives an overview of the Azure Percept platform capabilities and components, explains how it solves a key problem for edge AI developers today, and concludes with a list of relevant resources to jumpstart your own prototyping journey.
Did you know 65% of us are visual learners? Our brains are wired to absorb information from visual cues and use them to detect patterns and make connections faster, and with better recall. Visual guides offer a “big picture” summary of the topic that you can use as a pre-read (before diving into documentation) or a post-recap resource (to identify gaps in learning or coverage). Download a high-resolution image of the visual guide to Azure Percept and use it as desktop wallpaper or print it out as a handy reference to support your learning journey.
Edge Computing defines a distributed architecture with compute resources placed closer to information gathering resources to reduce network latency and bandwidth usage in cloud computing. By pairing an intelligent edge with an intelligent cloud, we get faster decision making, offline operation, optimized network usage, and data privacy protections. Edge AI uses edge compute resources to run machine learning and data analytics processes on-device (e.g., for real-time insights, intelligent decision-making, and workflow automation solutions) making such platforms critical to hybrid cloud strategies.
Many edge AI solutions today must be built from the ground up using diverse hardware (devices) and software (services) that need to be integrated and managed manually, creating workflow complexity for developers. Creating and deploying AI models also assumes a level of data science and machine learning expertise that many traditional IoT developers lack.
Azure Percept is an end-to-end technology platform from Microsoft that was designed to tackle these challenges, making it easier for IoT practitioners to rapidly prototype, deploy, and manage, their edge AI solutions. Azure Percept has three core aspects:
With Azure Percept, practitioners can build and deploy custom AI models, setup and manage IoT device collections, and integrate seamlessly with a rich set of Azure cloud services – for edge AI application prototyping & deployment at scale. Azure Percept fits seamlessly into familiar Azure IoT architectures, lowering the learning curve for adoption. It supports rich tools and documentation for low-code development, so developers can build & deploy edge AI solutions without needing deep data science or cloud computing expertise. Let's explore the visual guide to Azure Percept!
The illustrated guide below gives a visual summary of the Azure Percept Overview documentation. I recommend you download the high resolution image of this guide and use it as a reference for the rest of this post.
In the next few sections, we’ll explore some sections of the visual guide (with relevant links for self-guided deep dives) with a focus on three aspects: the big picture, the core components, and next steps to get started! Let's dive in!
Azure Percept is a family of hardware, software and services that covers the full stack from silicon to services, to help customers solve integration challenges of using AI at the edge, at scale. It tackles three main points of friction for edge AI integrations:
To achieve this, the Azure Percept platform provides support for on-device prototyping (dev kit), cross-device workflow and solution management (portal), and guidance for best practices in each case. Let’s look at the three aspects briefly:
To get started prototyping your edge AI applications or solutions, you’ll need access to suitable device hardware and an Azure account for service integration and solution management needs.
Start by logging into Azure Percept Studio portal – you'll see an entry page like the one below.
The "Create a Prototype" option is a good place to start your prototyping journey. The Azure Percept Studio portal provides computer vision and speech (voice assistant) tutorials with a low-code approach (using visual interfaces & templates for interactive configuration) that walks you through the process of exploring sample AI models, creating custom models, and deploying these prototypes to your edge devices – all from one unified interface. Sample models exist for people detection, vehicle detection, general object detection and products-on-shelf detection use Azure IoT Hub and Azure IoT Edge service integrations for seamless deployment.
Interested in deploying AI at the Edge in your solutions and projects? We’d love to hear from you about the application domain and usage scenarios and keep you updated on what’s coming next. Drop a comment below, subscribe to the blog, stay in touch!
Thanks for reading! This was a quick visual introduction to Azure Percept – an end-to-end edge AI platform that supports the “Sense. Know. Act.” requirements for intelligent edge applications!
Want to learn more on your own? Here are some relevant resources to get you going:
Azure IoT Fundamentals:
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