☁️Cloud
Cloud architecture, services and how we deploy our application on cloud.
Architecture
We use Hybrid Cloud with private cloud (on premise) and public cloud (Microsoft Azure). they are connected through LANs, WANs or APIs.
Services
We use Azure Cloud Service in our public cloud by following services
Web
Static Web Apps: To deploy front-end and back-end by use code editor extension.
Storage
Azure Files: To store files from application.
Blob Storage: To store images.
Database
Azure SQL Database: To store database for APIs
SQL Server on Azure VM: To manage VM in public cloud
Containers
Azure Funtions: A service in Static Web Apps for deploying APIs
Security
Application Gateway: Gateway which includes WAF (Web application Firewall) and Load Balancer. WAF can protect web applications from web vulnerabilities and attacks.
VPN Gateway: To connect with on premise network.
DDos Protection: to protect your Azure resources from denial of service (DoS) attacks with always-on monitoring and automatic network attack mitigation.
NSG (Azure Network Security Groups): To allow or deny inbound or outbound network traffic in virtual network on Azure Cloud.
DevOps
Azure Monitor: to help to maximize the performance of applications and services by collecting, analyzing informations in the cloud.
Machine learning
Microsoft Azure Machine Learning Studio: Help to manage all about machine learning like deploying, automated train the model etc.
Deployment
Machine Learning Services
Deployment
Application and API which be stored in github repository will be deployed by using source control like Github Action or azure DevOps Piplines to Azure service that is Azure Static Web Apps
Microsoft Azure Machine Learning Studio: GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure.
we use it for deploying and collecting source code for preprocessing and training Model
Ref : https://studio.azureml.net/
Automated Training
Microsoft Azure Automated Machine Learning: Automate time-consuming and iterative tasks of machine learning model development.
we use it for getting warnings from azure monitor about time to maintain model. after that, send the retrained model into git repo for our developer.
Azure Monitor: Used to maximize the availability and performance of your applications and services. It delivers a comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.
for monitoring about metrics of the model and constraints and then sending this information to retrained model
Ref of Microsoft Azure Automated Machine Learning: https://azure.microsoft.com/en-us/products/machine-learning/automatedml/#features
Ref of Azure Monitor: https://learn.microsoft.com/en-us/azure/azure-monitor/overview
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