Hands-on workshop to create containers-based backend platform that exposes async AI services
| Service | Build | Release |
|---|---|---|
| Cognitive Orchestrator | ||
| Cam-Frame Analyzer | ||
| Crowd Analyzer | ||
| Core Lib | ||
| Cognitive Services Helper |
Almost all enterprises already operate networked CCTV cameras all over buildings and stores. Crowd Analytics Platform extend these cameras capability to include some useful intelligence on top of it to improve customers experiences and safety trough AI.
NOTE: It is important to mention before diving into the platform that leveraging and designing AI powered solutions should always adhere to responsible and ethical principles that reflect trust and timeless values. I personally like Microsoft's principles about fairness, inclusiveness, reliability, safety, transparency, privacy, security and accountability. Read more
Crowd analytics (also known as footfall analytics) is about understanding who (age, gender,...) are visiting your location, when and how many.
This workshop walks through a guided deployment of a connected, secure and reliable Crowd Analytics platform leveraging cloud native technologies.
Crowd analytics scenario can be used in:
- Retail shops in many industry (like telecom shops, consumer goods retailers, malls…)
- Public and Private Parks
- Events
- Building safety and evacuation readiness
- Any many other scenarios
Using advanced cloud technologies can provide key business metrics that allows stakeholders to make informative decisions to improve experience and/or performance.
Azure already provides a sophisticated platform to develop and operate such system.
Kubernetes became the defacto cloud native technology to develop and operate sophisticated containerized systems anywhere and with Azure Kubernetes Service takes a lot of the cluster management tasks out of your way to focus more on the solution you want to build.
Key technologies used in the workshop include:
- Azure Kubernetes Service (AKS)
- Azure Container Registry (ACR)
- Azure Service Bus
- Azure IoT Hub
- Azure Cognitive Services
- Azure Storage
- Azure DevOps
- Power BI
- and many more
I'm really excited to share with you the great journey I took to build such system.
- Dev Environment Setup
- Visual Studio Code
- Azure CLI
- Other tools
- Solution Whiteboarding & Architecture
- Walkthrough the system objectives
- Review Azure architecture
- Creating Azure Prerequisites
- Resource Group
- Storage Account
- Cosmos DB
- Service Bus
- Cognitive Service
- Container Registry
- Virtual Network
- Log Analytics
- App Insights for each app
- AKS Cluster
- Essential kubernetes cluster provisioning
- Kubernetes cluster preparations
- Creating IoT Hub and IoT Edge Device
- IoT Hub Provisioning
- Message Routing to Service Bus
- Device Options
- Workshop Device Setup
- Services Deployment & Testing
- Connecting to Azure
- Source Code
- Continuos Integration
- Continuos Delivery
- Testing
- Data Visualization
- Power BI Desktop
- Connecting to Cosmos Db
- Query edit and data prep
- Design the report
- Publish the report
- BONUS Tracks (coming soon)
- Advanced AKS Provisioning
- AKS Preview Features
- Azure AD Integration
- Helm initialization
- Node pools (with Windows Pool)
- Virtual Nodes
- Application Gateway Ingress Controller
- Azure Firewall and Egress Lockdown
- API Management
- Azure AD Integration
- Realtime Communication
- Cosmos DB Change Feed
- SignalR client communication
- IoT Edge Deployment
- IoT Edge on Kubernetes
- Custom Modules
- IoT Edge on Raspberry Pi with USB Camera
- Advanced AKS Provisioning
| Topic | Duration |
|---|---|
| Registration and Setup | 15 mins |
| Why Intelligent Experiences? | 20 mins |
| Intelligent Retail - Crowd Analytics Overview | 25 mins |
| Break | |
| Solution White-boarding | 30 mins |
| Lab - Azure Services Provisioning | 60 mins |
| Lab - AKS Provisioning | 60 mins |
| Lunch | |
| Lab - IoT Hub Provisioning | 30 mins |
| Lab - DevOps & Services Deployment | 90 mins |
| Break | |
| Lab - Dashboard & Data Visualization | 30 mins |
| Q and A + Next Steps | 30 mins |
| Bonus Workshop Material (Take home tasks) | |
| Bonus 1 - Advanced AKS Provisioning | - |
| Bonus 2 - IoT Edge Development | - |
I tried to make sure I cover all aspects and best practices while building this project, but all included architecture, code, documentation and any other artifact represent my personal opinion only. Think of it as a suggestion of how a one way things can work.
Keep in mind that this is a work-in-progress, I will continue to contribute to it when I can.
All constructive feedback is welcomed 🙏
You can always create issue, suggest an update through PR or direct message me on Twitter.
Part of this workshop source code is based on Cognitive-Samples-IntelligentKiosk. Visit the GitHub repo to know more about all of the great capabilities of Azure Cognitive Services.
Using a webcam through HTML5/JS code based partially on the great demo from DaveVoyles
 |
|---|
| Mohamed Saif |
| GitHub |
| Blog |
All Intelligent Experiences On Containers documentation and samples are licensed with the MIT License. For more details, see LICENSE
The image, voice, video or text understanding capabilities of the Intelligent Kiosk Sample uses Microsoft Cognitive Services. Microsoft will receive the images, audio, video, and other data that you upload (via this app) for service improvement purposes. To report abuse of the Microsoft Cognitive Services to Microsoft, please visit the Microsoft Cognitive Services website at https://www.microsoft.com/cognitive-services, and use the “Report Abuse” link at the bottom of the page to contact Microsoft. For more information about Microsoft privacy policies please see their privacy statement here: https://go.microsoft.com/fwlink/?LinkId=521839.
Developers using Cognitive Services, including this sample, are expected to follow the “Developer Code of Conduct for Microsoft Cognitive Services”, found at http://go.microsoft.com/fwlink/?LinkId=698895