Essentials for building and modernizing AI apps on Azure

In a recent blog, we outlined what Azure Essentials is and why it matters. This blog will take a closer look at how Azure Essentials helps organizations build and modernize AI applications. We will continue with the three-stage structure of Readiness and Foundation, Design and Govern, and Manage and Optimize, designed to guide organizations through the entire cloud adoption journey.

Before we dive in, let’s take a moment to recap what we’ve learned since: as the pace of AI innovation accelerates, increasingly, we witness enterprises building AI apps to deliver business values. This may include personalized shopping experiences, document intelligence, fraud detections, or building their own Copilots. While such use of AI in apps is growing rapidly, we also hear a multitude of new challenges from customers. When they invest into AI applications, some face a shortage ​of skilled developers, long ​release ​cycle, or a host of legacy app ​and data architectures.

A smooth transition to AI apps requires tools for reliable deployments, a modernization strategy for existing data estates, best practices on pricing, and most important of all, security. Overwhelmed? Azure Essentials is here to help. 

What is Azure Essentials? 

Azure Essentials combines Microsoft’s proven practices, product expertise, reference architectures, training, and comprehensive resources into one central hub to help you optimize your Azure investments. Built on industry and Microsoft’s own experience, Azure Essentials offers detailed guidance for various business scenarios.

It supports the enhancement of current deployments by offering clear pathways to strengthen workload resiliency, security, and cost-efficiency. In short, it is a holistic set of resources to support organizations unlock value from AI apps.

As consistent across Azure Essentials, for building and modernizing AI apps are categorized into three revolving steps: Readiness and Foundation, Design and Govern, and Manage and Optimize.  

Stage 1: Readiness and foundation 

The Readiness and Foundation stage is the cornerstone for developing and modernizing AI applications on Azure. This phase focuses on evaluating business processes, securing resources, and preparing for seamless migration. By leveraging tools like Azure Migrate and landing zones, organizations can ensure robust scalability, security, and cost-efficiency. Additionally, adopting FinOps practices, strengthening compliance, and skilling up teams lay the groundwork for future-proof AI app success. Let Azure Essentials guide your transformation journey effectively. Opportunities to consider include:
 

• Tools for Ideation and Assessments: Start by evaluating existing business processes, customer interactions, and data assets to identify areas where AI can make a significant impact. Tools like Azure Migrate and Azure Well-Architected Review enable detailed assessments, guiding migration strategies and cost estimations.
   
• Landing Zones: Azure landing zones provide pre-configured environments with built-in security and governance. These zones streamline app migration and ensure compliance with enterprise standards.
   
• Financial Best Practices: Adopt FinOps to manage cloud costs effectively. Use tools like Azure Hybrid Benefit to reduce licensing costs and optimize resource usage throughout the cloud journey.
   
• Security and Compliance: Integrate security early by leveraging the Secure Methodology and Microsoft’s Cloud Adoption Framework, ensuring incremental improvement over time.
   
• Skilling and Learning Resources: Upskill your team with role-based training assets like “Modernize Apps for AI-Readiness” and interactive labs. Explore the AI Learning Hub for comprehensive, technical training resources.

Stage 2: Design and govern 

The Design and Govern phase is critical for building scalable, secure, and efficient AI applications on Azure. This stage focuses on modernizing data and app estates, implementing robust application patterns, managing APIs effectively, and optimizing database requirements. By leveraging Azure's proven tools like API Management, Verified Modules, and best practices for Responsible AI, organizations can ensure seamless integration and governance. Discover how to streamline your cloud journey while ensuring high performance, reliability, and compliance with Azure Essentials. Opportunities to consider include:
 

• Data and App Estate Modernization: Modernization involves aligning processes, systems, and strategies with cloud-native principles. Use resources like the Forrester Application Modernization Checklist and Azure Verified Modules (AVM) for structured modernization.
   
• Application Patterns: Design cloud-native apps with Azure application patterns for scalability and reliability. Resources such as Azure Pipelines baseline architecture and Azure App Configuration simplify implementation.
   
• API Management: Implement Azure API Management (APIM) to facilitate secure, scalable, and efficient integration between AI services and applications. This is essential for optimizing AI consumption costs and maintaining performance.
   
• Database Requirements: Choose the right database solutions based on performance, scalability, and data type. Resources like the Microsoft Intelligent Data Platform and Azure Architecture Center guide the selection process.
   
• AI Considerations: Adopt practices like MLOps and Responsible AI to ensure scalable, reliable, and ethical AI deployment. Azure Machine Learning supports MLOps for continuous integration and delivery of ML models.

Stage 3: Manage and optimize 

Efficiently managing and optimizing AI applications is critical for ensuring peak performance, reliability, and cost-effectiveness. Azure Essentials offers a suite of powerful tools to streamline testing, enhance application health, fine-tune databases, and optimize performance. From robust monitoring with Azure Monitor to database tuning and AI performance enhancements, this stage helps organizations maintain resilient, scalable AI solutions. Learn how these advanced practices empower businesses to maximize AI app efficiency while delivering seamless, high-quality experiences to users. Opportunities to consider include:
 

• Application Testing: Ensure AI models perform securely and efficiently with testing tools like Red Teaming for LLMs and Load Testing for RAG-Based Generative AI Applications.
   
• Application Health: Use Azure Monitor and Application Insights for proactive monitoring of AI applications to maintain optimal performance and reliability.
   
• Database Tuning: Optimize databases to reduce costs and enhance query performance. Techniques like semantic caching and query performance monitoring help minimize database read times and LLM token consumption.
   
• AI Performance Optimization: Leverage Azure tools such as OpenAI Service Performance Guidelines to enhance model performance by minimizing latency and improving throughput.

Before you go

Azure Essentials simplifies the complex journey of building and modernizing AI applications. From foundational readiness to robust management and optimization, its structured approach ensures organizations can unlock the full potential of AI.

To explore more, visit the Azure Essentials Hub for ongoing updates and in-depth guidance.