Knowledge Graph — Coursera Notes › Organizations › Microsoft
Azure
service · part of Microsoft
A public cloud provider offering scalable deployment platforms for applications of any size.
In ML workflows, a combination of storage solutions can streamline data processing and model training. For example: store raw data in Blob Storage, transform it using Data Lake Storage, save cleaned structured data in Azure SQL Database for easy querying, use Cosmos DB for real-time data for instant predictions, and use Azure File Storage to share artifacts and logs with the team.
Cloud computing platform by Microsoft. Provides services for deploying and securing ML models, including encryption, private endpoints, RBAC, network security groups, and continuous monitoring via Azure Security Center.
An environment with inadequate protections for data, ML models, and services. Common issues: lack of encryption, improper access control, exposed endpoints. Risks include data breaches, model theft, and malicious manipulation.
Use Azure Private Link to keep endpoints private, avoiding exposure to public networks. Ensures only authorized internal resources can communicate with the deployment.
Control inbound and outbound traffic. Limit access to critical resources based on trusted IP addresses.
Use Azure Security Center to monitor and assess security status continuously. Regularly audit access logs and update security policies to mitigate emerging threats.
Inside Azure (24)
- Azure ML Service — A cloud-based environment for training, deploying, and managing machine learning models.
- Azure Kubernetes Service (AKS) — A managed Kubernetes service for deploying and scaling containerized applications.
- Azure account — An entry point to the Azure ecosystem providing access to cloud services and a centralized dashboard (Azure Portal) for managing resources.
- Azure Databricks — An Apache Spark-based analytics platform for data processing and transformation.
- Virtual machines — Software-based emulations of physical computers in Azure, allowing isolated environments for installing and configuring AI/ML software.
- ARM template — Azure Resource Manager template that captures environment configuration in a reusable format for automated deployment and consistency.
- Azure App Service — A service for deploying web applications and APIs, providing an easy-to-manage environment with integrated deployment pipelines.
- Azure App Services — A platform for building and hosting web apps and REST APIs.
- Azure Batch — A service for processing large volumes of data or parallel execution of multiple models across many virtual machines.
- Azure Blob Storage — A cloud object storage solution for unstructured data such as images and backups.
- Azure Container Instances (ACI) — A serverless container service for running containers without managing infrastructure, often used for deploying Azure ML models.
- Azure Cosmos DB — Azure Cosmos DB is a fully managed NoSQL database service for semi-structured data, offering global distribution and multi-model support.
- Azure Data Factory — A cloud-based data integration service for orchestrating and automating data movement and transformation.
- Azure Data Lake Storage — A scalable and secure data lake for high-performance analytics workloads.
- Azure DevOps — A set of tools for CI/CD integration, enabling smooth updates, rollbacks, and automated testing for model deployments.
- Azure Event Hubs — A high-throughput ingestion service capable of handling millions of events per second for real-time streams.
- Azure File Storage — Azure File Storage offers fully managed file shares accessible via the standard SMB protocol, suitable for applications requiring shared access to files across multiple virtual machines.
- Azure Functions — Serverless compute service that runs on demand per event for lightweight, intermittent models.
- Azure Machine Learning Studio — A low-code/no-code graphical environment for easy model deployment, suitable for teams with limited cloud expertise.
- Azure Monitor — A service for monitoring the performance and health of applications and infrastructure.
- Azure SQL Database — A fully managed relational database with built-in intelligence for structured data.
- Resource group — A container that holds related resources for an Azure solution.
- Resource groups — Logical containers for resources sharing the same life cycle, enabling collective management, monitoring, access control, and cost tracking.
- Virtual Machine — An Azure compute resource that runs an operating system and applications.
Connections
- Related to Scalability
- Related to Cloud inference
- Related to Azure account
- Alternative to Github repository
- Used for Virtual Machine
- Related to Artificial Intelligence
- Alternative to Google Cloud
- Used for Kubernetes Deployment
- Related to APIs
- Related to Security
- Related to Cost-effectiveness
- Related to Reliability
- Related to Total Cost of Ownership
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