Azure AI Ready Infrastructure

• High-performance connectivity: AI-ready workloads demand low latency and high bandwidth. Azure’s network infrastructure ensures that data can flow smoothly between services, regions, and hybrid environments.
• Virtual networks and ExpressRoute: These services provide secure, scalable connectivity, enabling reliable communication between AI services, data sources, and applications.
• Load balancing and traffic management: Azure’s network load balancers and traffic managers ensure that AI applications maintain high availability, distributing traffic intelligently across resources.

• Landing zone: The AI-ready landing zone includes pre-configured infrastructure, integrating best practices for network, security, and identity management. It serves as the foundation for building and deploying AI models and applications.
• Compute and storage: Azure offers scalable compute options like Virtual Machines, Kubernetes Service (AKS), and AI-specific GPUs, allowing for flexible deployment of AI models. Azure Blob Storage, Azure Data Lake Storage, and Azure NetApp Files provide the necessary storage solutions for AI data.
• Azure Virtual Desktop (AVD): AVD offers remote access to AI-powered apps, enabling efficient use of AI tools from any location, supporting a flexible workforce.
• Migration services: Azure provides seamless migration tools to move existing workloads to the AI-ready infrastructure, including Azure Migrate, which automates and simplifies the process.

Azure Data Fabric: The data fabric in Azure offers unified data management, enabling easy data access, governance, and orchestration across diverse sources, crucial for AI model training.

OneLake: Integrated with Azure Synapse Analytics, OneLake serves as a centralised data lake, supporting AI workloads by aggregating structured and unstructured data in a single repository.

Azure Databricks: This collaborative AI environment supports data engineering, machine learning, and analytics, enabling rapid AI model development and deployment.

Azure Purview: Azure Purview provides enterprise-wide data governance, ensuring data is consistently tracked, classified, and managed, which is vital for AI operations.

Databases: Azure’s managed database services, such as SQL Database, Cosmos DB, and Synapse, support real-time analytics, transaction processing, and large-scale AI model training.

• Microsoft Entra: Entra is at the core of identity management, enabling secure access to AI services and data with advanced authentication and access controls.
• Microsoft Defender for Cloud: This tool offers advanced threat detection and security posture management for AI workloads, identifying and mitigating security risks.
• Microsoft Sentinel: Sentinel provides security information and event management (SIEM) for AI applications, ensuring AI workloads are monitored and protected in real time.
• Azure Key Vault: Azure Key Vault secures sensitive AI data, models, and encryption keys, offering robust protection and management for AI-related secrets.
• Azure Policy: Azure Policy enforces governance and compliance for AI environments, ensuring that workloads align with organizational and regulatory requirements.

• Azure Arc: Azure Arc extends AI-ready infrastructure management to hybrid and multi-cloud environments, enabling consistent operations, AI model deployment, and governance across clouds.
• FinOps Tools: Azure’s FinOps capabilities offer AI-specific cost tracking, resource optimisation, and predictive analytics, ensuring efficient financial management for AI workloads.
• Disaster Recovery (DR) and High Availability (HA): Azure’s built-in DR and HA features ensure that AI workloads are resilient, maintaining operations even during service disruptions or outages.