You're done with your proof-of-concept and ready to scale your AI adoption. But how and what do you need to prepare? This blog gives a framework and overview of how to leverage Azure Essentials to get you on the right path.
","body":"As the demand for scalable, efficient AI cloud solutions grows, many organizations are looking to be AI-Ready. AI is not only a powerful tool for innovation and insights, but also a driver for cloud migration and modernization. Azure Essentials helps customers accelerate the reliability, security, and ongoing performance of AI and cloud investments. Whether you are new to AI or looking to scale your existing AI solutions, Azure Essentials offers comprehensive guidance and resources all in one place to help you succeed.
\n\n
We recognize the distinct challenges and benefits of integrating AI with cloud infrastructure and are committed to delivering a comprehensive, expert-led experience including best practices and customized tools to guide you through the AI adoption journey.
\n\n
Becoming AI ready implies that an organization scale AI adoption. The guidance within Azure Essentials for AI outlines a strategy in three stages: establishing a solid foundation and readiness, correctly designing and incorporating governance into AI workloads, and managing and optimizing deployments. Within each of these stages we provide a comprehensive set of resources including detailed best practices, frameworks, and tools for ensuring reliability, security, performance and efficiency.
\n\n
\n
\n
Before embarking on AI development, it's essential to assess your business needs and pinpoint where AI can deliver significant benefits. Use cases vary, from enhancing customer support to streamlining supply chain operations. It's your decision to determine how to utilize genAI effectively both immediately and in the future. The GenAI Readiness assessment helps you evaluate your strategy and AI adoption. By defining clear objectives, you can devise an AI strategy that complements your business goals. This stage guides you through five key considerations to start on the right foot:
\nAfter laying the groundwork, it's time to roll up your sleeves and start constructing your AI solutions. This stage is focused on Designing and Governing your AI projects, and consists of five key considerations:
\nThis all comes together in Azure AI studio which provides a complete toolchain including model choice & benchmarks, prompt catalogs, responsible AI, and ongoing management. See for more details: What is Azure AI Studio? - Azure AI Studio | Microsoft Learn
\n\n
As your AI adoption progresses and scales, it's essential to continuously enhance and refine your workloads. The third stage of our Azure Essentials AI adoption guidance focuses on Managing and Optimizing your deployments, concentrated on 3 key considerations:
\nDetermine how ready your organization is to scale AI adoption with the Technical Assessment for Generative AI in Azure. This comprehensive assessment evaluates your AI preparedness for projects of all sizes, providing you with pragmatic recommendations based on your specific needs.
\nIf you have an AI project in mind, and you’d like expert assistance, consider leveraging one of our advanced specialized partners with Microsoft’s Azure Innovate offering. Or if you’re looking to migrate workloads to adopt AI at scale, consider leveraging Microsoft’s Azure Migrate and Modernize offering.
\nIf you have a Microsoft Unified Support agreement in place, you can get personalized assistance by contacting your Microsoft Customer Success Account Manager (CSAM), who can help you maximize the benefits of Azure for your AI initiatives.
\n\n
Review the Azure Essentials official website for additional information.
\n\n
\n
Check out the following resources to explore further:
\n\n
Migration resources: Build a business case for migrating to innovate with AI | Azure Migrate Overview | Migration tools | Migration FAQ
\nLanding Zone Resources: Landing Zone deployment options | ISV considerations | Landing zone FAQ
\nFinOps Resources: Cost optimization of AI workloads | How to forecast AI services costs in cloud | FinOps Framework | FinOps Review
\nPricing Resources: Azure Pricing Calculator | PTU Reservations Blog | Azure OpenAI sizing tool | Pricing Blog
\nSkilling Resources: Azure AI Fundamentals | Azure AI Engineer Associate | Azure Data Scientist Associate | Transform your business with Microsoft AI
\n\n
Responsible AI principles: Responsible AI Toolbox | Azure Confidential Computing | A general overview of responsible AI Tools and Practices | Blog: Tools for Responsible AI
\nContent Safety: Azure AI Content Safety | Learn how to build responsible AI | Responsible AI Impact Assessment | Azure AI Content Safety - Pricing
\nData governance, security, and compliance: Introducing modern data governance for the era of AI | Best practice for data and AI governance | Unpacking AI governance to shape digital transformation
\nArchitectural guidance: Baseline OpenAI end-to-end chat reference architecture | Access Azure OpenAI and other language models through a gateway | Azure OpenAI chat baseline architecture in an Azure landing zone
\nLLMOps: LLMops Assessment to determine your organization’s LLMOps maturity | LLMOps with prompt flow and GitHub | Designing and developing a RAG solution | Retrieval Augmented Generation using Azure Machine Learning prompt flow | Build apps with LLM Workshop
\n\n
Monitoring and security: Model monitoring for generative AI applications | Inference data collection from models in production | Shared Responsibility AI
\nManagement and optimization: Optimizing Generative Applications on Azure Open AI | Monitoring with Azure OpenAI Studio | Service Benchmarking tool | Solution sizing tool | Resource utilization and efficiency with Microsoft Cost Management | Understand and optimize costs w/ the Cost optimization workbook | Learn how to optimize for carbon using Azure carbon optimization
\nArchitectural assessments & remediations: An overview of Azure Advisor | Well-Architected Review - MLOps | Learn how to conduct and analyze results from groundedness tests | Leverage the GovViz Tool to keep Azure policies up to date
\n\n
\n
\n
","body@stringLength":"31315","rawBody":"
As the demand for scalable, efficient AI cloud solutions grows, many organizations are looking to be AI-Ready. AI is not only a powerful tool for innovation and insights, but also a driver for cloud migration and modernization. Azure Essentials helps customers accelerate the reliability, security, and ongoing performance of AI and cloud investments. Whether you are new to AI or looking to scale your existing AI solutions, Azure Essentials offers comprehensive guidance and resources all in one place to help you succeed.
\n\n
We recognize the distinct challenges and benefits of integrating AI with cloud infrastructure and are committed to delivering a comprehensive, expert-led experience including best practices and customized tools to guide you through the AI adoption journey.
\n\n
Becoming AI ready implies that an organization scale AI adoption. The guidance within Azure Essentials for AI outlines a strategy in three stages: establishing a solid foundation and readiness, correctly designing and incorporating governance into AI workloads, and managing and optimizing deployments. Within each of these stages we provide a comprehensive set of resources including detailed best practices, frameworks, and tools for ensuring reliability, security, performance and efficiency.
\n\n
\n
Before embarking on AI development, it's essential to assess your business needs and pinpoint where AI can deliver significant benefits. Use cases vary, from enhancing customer support to streamlining supply chain operations. It's your decision to determine how to utilize genAI effectively both immediately and in the future. The GenAI Readiness assessment helps you evaluate your strategy and AI adoption. By defining clear objectives, you can devise an AI strategy that complements your business goals. This stage guides you through five key considerations to start on the right foot:
\nAfter laying the groundwork, it's time to roll up your sleeves and start constructing your AI solutions. This stage is focused on Designing and Governing your AI projects, and consists of five key considerations:
\nThis all comes together in Azure AI studio which provides a complete toolchain including model choice & benchmarks, prompt catalogs, responsible AI, and ongoing management. See for more details: What is Azure AI Studio? - Azure AI Studio | Microsoft Learn
\n\n
As your AI adoption progresses and scales, it's essential to continuously enhance and refine your workloads. The third stage of our Azure Essentials AI adoption guidance focuses on Managing and Optimizing your deployments, concentrated on 3 key considerations:
\nDetermine how ready your organization is to scale AI adoption with the Technical Assessment for Generative AI in Azure. This comprehensive assessment evaluates your AI preparedness for projects of all sizes, providing you with pragmatic recommendations based on your specific needs.
\nIf you have an AI project in mind, and you’d like expert assistance, consider leveraging one of our advanced specialized partners with Microsoft’s Azure Innovate offering. Or if you’re looking to migrate workloads to adopt AI at scale, consider leveraging Microsoft’s Azure Migrate and Modernize offering.
\nIf you have a Microsoft Unified Support agreement in place, you can get personalized assistance by contacting your Microsoft Customer Success Account Manager (CSAM), who can help you maximize the benefits of Azure for your AI initiatives.
\n\n
Review the Azure Essentials official website for additional information.
\n\n
\n
Check out the following resources to explore further:
\n\n
Migration resources: Build a business case for migrating to innovate with AI | Azure Migrate Overview | Migration tools | Migration FAQ
\nLanding Zone Resources: Landing Zone deployment options | ISV considerations | Landing zone FAQ
\nFinOps Resources: Cost optimization of AI workloads | How to forecast AI services costs in cloud | FinOps Framework | FinOps Review
\nPricing Resources: Azure Pricing Calculator | PTU Reservations Blog | Azure OpenAI sizing tool | Pricing Blog
\nSkilling Resources: Azure AI Fundamentals | Azure AI Engineer Associate | Azure Data Scientist Associate | Transform your business with Microsoft AI
\n\n
Responsible AI principles: Responsible AI Toolbox | Azure Confidential Computing | A general overview of responsible AI Tools and Practices | Blog: Tools for Responsible AI
\nContent Safety: Azure AI Content Safety | Learn how to build responsible AI | Responsible AI Impact Assessment | Azure AI Content Safety - Pricing
\nData governance, security, and compliance: Introducing modern data governance for the era of AI | Best practice for data and AI governance | Unpacking AI governance to shape digital transformation
\nArchitectural guidance: Baseline OpenAI end-to-end chat reference architecture | Access Azure OpenAI and other language models through a gateway | Azure OpenAI chat baseline architecture in an Azure landing zone
\nLLMOps: LLMops Assessment to determine your organization’s LLMOps maturity | LLMOps with prompt flow and GitHub | Designing and developing a RAG solution | Retrieval Augmented Generation using Azure Machine Learning prompt flow | Build apps with LLM Workshop
\n\n
Monitoring and security: Model monitoring for generative AI applications | Inference data collection from models in production | Shared Responsibility AI
\nManagement and optimization: Optimizing Generative Applications on Azure Open AI | Monitoring with Azure OpenAI Studio | Service Benchmarking tool | Solution sizing tool | Resource utilization and efficiency with Microsoft Cost Management | Understand and optimize costs w/ the Cost optimization workbook | Learn how to optimize for carbon using Azure carbon optimization
\nArchitectural assessments & remediations: An overview of Azure Advisor | Well-Architected Review - MLOps | Learn how to conduct and analyze results from groundedness tests | Leverage the GovViz Tool to keep Azure policies up to date
\n\n
\n
\n
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You're done with your proof-of-concept and ready to scale your AI adoption. But how and what do you need to prepare? This blog gives a framework and overview of how to leverage Azure Essentials to get you on the right path.
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