Merge pull request #6079 from MicrosoftDocs/main

12/9/2024 AM Publish

docs/scenarios/ai/index.md

@@ -1,6 +1,6 @@
 ---
 title: AI adoption
-description: Learn how Microsoft recommends adopting generative AI, nongenerative AI, and machine learning.
+description: Learn the process to adopt generative AI and nongenerative AI for startups and enterprises.
 author: stephen-sumner
 ms.author: ssumner
 ms.date: 11/01/2024
@@ -9,31 +9,35 @@ ms.topic: conceptual
 
 # AI adoption
 
-This AI guidance provides a structured approach for integrating AI into your organization using Azure. The guidance is relevant for organizations of any size and industry. It covers both generative and nongenerative AI adoption guidance. It is your roadmap for AI adoption and a central hub to find the resources you need.
+This AI guidance provides a roadmap for [startups](https://www.microsoft.com/startups) and enterprises to adopt and maintain AI. It provides best practices. These best practices cover AI technology decisions (build vs. buy), skill development, team organization, and processes to govern, manage, and secure AI.
 
 :::image type="content" source="./images/ai-adoption-process.svg" alt-text="Diagram showing the AI adoption process: AI Strategy, AI Plan, AI Ready, Govern AI, Manage AI, and Secure AI." lightbox="./images/ai-adoption-process.svg" border="false":::
 *Figure 1. How to use the AI adoption guidance.*
 
 ## Why adopt AI?
 
-AI adoption is foundational to optimizing operations for individuals and organizations. Effective AI adoption uses governance, security, and management best practices to build trustworthy AI workloads. It enhances individual efficiency by automating routine tasks and providing creative insights. AI automates business processes, ranging from adaptive user experiences to business forecasting. These use cases allow businesses and employees to focus on customers and work that matters most. You have options to adopt AI the way that best helps your organization. Microsoft has AI services for different skill set, budgets, and data needs. You can decide what works best based on your goals. This guidance helps you navigate these decisions.
+AI improves efficiency through automation. This automation can boost individual productivity, and it can optimize a range of business processes. A successful AI implementation allows you to focus on priorities that drive your organization forward.
 
 ## How to adopt AI
 
-Start by identifying the need for AI in your organization. Look at the common and essential workflows, tasks, and processes in your business. Evaluate if there are opportunities to automate with AI. When you have general direction, then you're ready to adopt AI.
+Begin with [AI Strategy](./strategy.md) to identify use cases where AI can deliver value. Based on that analysis, determine whether to build (PaaS and Iaas) or buy (SaaS) AI solutions for each use case. Implement your strategy using the [AI Plan](./plan.md) to transition identified use cases into production.
 
-AI adoption is a sequential process. This guidance divides AI adoption into six steps. If you're new to AI adoption, complete all six steps. As you define new AI use cases for your organization, revisit the AI Strategy, AI Plan, and AI Ready. Govern AI, Manage AI, and Secure AI are continuous processes. You need to iterate through these processes to ensure your AI workloads remain secure, cost-effective, and trustworthy.
+If you choose to ***build*** AI workloads with Azure PaaS or IaaS services, follow the [AI Ready](./ready.md) guidance to establish an AI foundation in Azure. If you opt to ***buy*** a Microsoft Copilot SaaS solution, skip the Ready AI guidance.
 
-- *AI Strategy*: Guidance on how to identify the need for AI and then choose the right AI technology for your business and ground AI adoption in responsible AI principles.
-- *AI Plan*: Guidance to assess and find the AI skills needed and how to prioritize use cases to bring to production.
-- *AI Ready*: Guidance to establish a governed and secure Azure environment for your AI workloads and data.
-- *Govern AI*: The process to control your AI workloads and models by establishing guardrails to ensure compliance and responsible AI use.
-- *Manage AI*: The process to manage your AI deployments, operations, models, and data over time to ensure they remain aligned with your business goals.
-- *Secure AI*: The process to assess AI security risks and apply security controls to protect your AI workloads.
+You must establish processes to [Govern AI](./govern.md), [Manage AI](./manage.md), and [Secure AI](./secure.md) to maintain and sustain trustworthy AI. Apply these processes to any AI solution.
+
+| AI adoption step | Description | Applicable to build (PaaS or IaaS) or buy (SaaS)? |
+|---|---|---|
+| AI Strategy | Guidance to pick the right AI solutions. | For build (PaaS/IaaS) and buy (SaaS) |
+| AI Plan | Guidance to execute AI adoption. | For build (PaaS/IaaS) and buy (SaaS) |
+| AI Ready | Guidance to build AI workloads in Azure environment. | For build (PaaS/IaaS) only |
+| Govern AI | Guidance to control AI. | For build (PaaS/IaaS) and buy (SaaS) |
+| Manage AI | Guidance to maintain AI. | For build (PaaS/IaaS) and buy (SaaS) |
+| Secure AI | Guidance to secure AI. | For build (PaaS/IaaS) and buy (SaaS) |
 
 ## AI checklists
 
-Use the AI checklists as your roadmap for adopting and maintaining AI. There are two checklists available: one for startups and one for enterprises. The enterprise checklist provides the most comprehensive guidance. It helps you prepare your organization to adopt AI at scale. For a quicker path to AI adoption, use the startup checklist. As your organization grows, consult the enterprise checklist to support your expanding AI needs.
+Use the AI checklists as your roadmap for adopting and maintaining AI. The enterprise checklist prepares your organization to adopt AI at scale. The startup checklist helps you move toward production faster but still get governance, management, and security best practices.
 
 | AI adoption phase | Startup checklist | Enterprise checklist |
 |---|---|---|

docs/scenarios/ai/infrastructure/governance.md

@@ -1,6 +1,6 @@
 ---
 title: Governance recommendations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to govern AI workloads on Azure infrastructure (IaaS)
+description: Learn how to govern AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/01/2024

docs/scenarios/ai/infrastructure/management.md

@@ -1,6 +1,6 @@
 ---
 title: Management recommendations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to manage AI workloads on Azure infrastructure (IaaS)
+description: Learn how to manage AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/01/2024

docs/scenarios/ai/infrastructure/networking.md

@@ -1,6 +1,6 @@
 ---
 title: Networking recommendations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to configure networking for AI workloads on Azure infrastructure (IaaS)
+description: Learn how to configure networking for AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/01/2024

docs/scenarios/ai/infrastructure/security.md

@@ -1,6 +1,6 @@
 ---
 title: Security recommendations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to secure AI workloads on Azure infrastructure (IaaS)
+description: Learn how to secure AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/01/2024

docs/scenarios/ai/infrastructure/storage.md

@@ -1,6 +1,6 @@
 ---
 title: Storage recommendations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to select storage for AI workloads on Azure infrastructure (IaaS)
+description: Learn how to select storage for AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/01/2024

docs/scenarios/ai/infrastructure/well-architected.md

@@ -1,6 +1,6 @@
 ---
 title: Well-architected considerations for AI workloads on Azure infrastructure (IaaS)
-description: Learn how to design AI workloads on Azure infrastructure (IaaS)
+description: Learn how to design AI workloads on Azure infrastructure (IaaS).
 author: stephen-sumner
 ms.author: rajanaki
 ms.date: 11/15/2024

docs/scenarios/ai/plan.md

@@ -28,7 +28,7 @@ In your [technology strategy](./strategy.md#define-an-ai-technology-strategy), y
 
 Acquiring AI skills requires organizations to assess their current talent pool and determine whether to upskill, recruit, or partner with external experts. Assess your current talent pool to identify needs for upskilling, recruiting, or external partnerships. Building a skilled AI team ensures you can adapt to challenges and handle various AI projects. AI constantly evolves, so maintaining a culture of continuous learning supports innovation and keeps skills current.
 
-- *Learn AI skills.* Use the [Microsoft Learn](/) platform for free AI [training](/training/), [certifications](/credentials/), and [product guidance](/docs/). Set certification goals, such as [Azure AI Fundamentals](/credentials/certifications/azure-ai-fundamentals/), [Azure AI Engineer Associate](/credentials/certifications/azure-ai-engineer/), and [Azure Data Scientist Associate](/credentials/certifications/azure-data-scientist/). There are learning resources for other subjects on the platform, so filter the results to return AI-specific results.
+- *Learn AI skills.* Use the [AI learning hub](/ai/) platform for free AI training, certifications, and product guidance. Set certification goals, such as earning [Azure AI Fundamentals](/credentials/certifications/azure-ai-fundamentals/), [Azure AI Engineer Associate](/credentials/certifications/azure-ai-engineer/), and [Azure Data Scientist Associate](/credentials/certifications/azure-data-scientist/) certifications.
 
 - *Recruit AI professionals.* For expertise beyond your internal capabilities, recruit AI professionals experienced in model development, generative AI, or AI ethics. These professionals are in high-demand. Consider collaborating with educational institutions to access fresh talent. Make sure to update job descriptions to reflect evolving AI needs, and offer competitive compensation. Create an attractive employer brand. Showcase your organization’s commitment to innovation and technological advancement, making your brand appealing to AI professionals.
 

docs/scenarios/azure-hpc/energy/storage.md

@@ -71,12 +71,12 @@ Energy MPI workloads have unique requirements with the need for low latency comm
 
 Parallel file systems such as Lustre are used for HPC energy workloads that require access to large files, simultaneous access from multiple compute nodes, and massive amounts of data. The implementation of parallel file systems makes it easy to scale in terms of capability and performance. Such file systems take advantage of RDMA transfers with large bandwidth and reduced CPU usage. The parallel file system is usually used as scratch space and intended for work that requires optimized I/O. Examples include workload setup, pre-processing, running, and post-processing.
 
-Using an orchestrated parallel file service, such as Azure Managed Lustre, works for 50,000 or more cores, with read/write rates up to 500 GB/s, and 2.5 PB storage.
+Using an orchestrated parallel file service, such as Azure Managed Lustre, works for 50,000 or more cores, with read/write rates up to 500 GB/s, and up to 12.5 PiB storage upon request.
 
 For more information on Parallel Virtual file system on Azure, see [Parallel Virtual File Systems on Microsoft Azure - Part 1: Overview - Microsoft Tech Community](https://techcommunity.microsoft.com/t5/azure-global/parallel-virtual-file-systems-on-microsoft-azure-part-1-overview/ba-p/306487).
 
  - Azure NetApp Files and local disks are typically used to handle the more latency/IOPS sensitive workloads, like seismic interpretation, model preparation, and visualization. Consider using  for workloads of up to 4,000 cores, with a throughput up to 6.5 GiB/s, and workloads that benefit from our require multiprotocol (NFS/SMB) access to the same data set.
- - Azure Managed Lustre provides faster and higher capacity storage for HPC workloads. This solution works for medium to very large workloads and can support 50,000 or more cores, with throughput up to 500 GB/s, and storage capacity up to 2.5 PiB.
+ - Azure Managed Lustre provides faster and higher capacity storage for HPC workloads. This solution works for medium to very large workloads and can support 50,000 or more cores, with throughput up to 500 GB/s, and storage capacity up to 12.5 PiB upon request.
  - Standard or Premium Blob is a cost effective being the lowest cost cloud offering. This service provides exabyte scale, high throughput, low latency access where necessary, familiar file system and multi-protocol access (REST, HDFS, NFS). You can make use of the NFS v3.0 at the blob service endpoint for high throughput and read heavy workloads.  You can optimize costs by moving to cooler tiers with the ability to perform lifecycle management with last update/ last access time, intelligent tiering with customizable policies.
  - The Oil and Gas energy workloads may also require large data size and volumes transfer mechanism from on-premises to Cloud and vice versa that can be achieved by
      - Offline - device based migration (DataBox)

docs/scenarios/azure-hpc/finance/storage.md

@@ -47,12 +47,11 @@ The following table provides a comparison of Blob Storage, Azure Files, Azure Ma
 | -- | -- | -- | -- | -- |
 | **Use cases** | Best suited for large-scale read-heavy sequential access workloads where data is ingested once and minimally modified. <br><br> Low total cost of ownership, if there's light maintenance. | A highly available service that's best suited for random access workloads. <br><br> For NFS shares, Azure Files provides full POSIX file system support. The built-in CSI driver enables you to easily use it from container platforms like Azure Container Instances and Azure Kubernetes Service (AKS), in addition to VM-based platforms. | Azure Managed Lustre is a fully managed parallel file system best suited to medium to large HPC workloads. <br><br> Enables HPC applications in the cloud without breaking application compatibility by providing familiar Lustre parallel file system functionality, behaviors, and performance, securing long-term application investments. | A fully managed file service in the cloud, powered by NetApp, with advanced management capabilities. <br><br> Azure NetApp Files is suited for workloads that require random access. It provides broad protocol support and improved data protection. |
 | **Available protocols** | NFS 3.0 <br><br>REST <br><br>Azure Data Lake Storage  | SMB <br><br> NFS 4.1 <br><br>(No interoperability between either protocol.) | Lustre | NFS 3.0 and 4.1 <br><br> SMB <br><br><br> |
-| **Key features** | Integrated with Azure HPC Cache for low-latency workloads. <br><br> Integrated management, including lifecycle management, immutable blobs, data failover, and metadata index. | Zonally redundant for high availability. <br><br> Consistent single-digit millisecond latency. <br><br> Predictable performance and cost that scales with capacity. | High storage capacity up to 2.5PB. <br><br> Low (~2ms) latency. <br><br> Spin up new clusters in minutes. <br><br> Supports containerized workloads with AKS. | Extremely low latency (as low as sub-millisecond). <br><br> Rich NetApp ONTAP management capability, like SnapMirror Cloud. <br><br> Consistent hybrid cloud experience. |
-| **Performance (per volume)** | As much as 20,000 IOPS. As much as 100 GiB/s throughput. | As much as 100,000 IOPS. As much as 80 GiB/s throughput. | As much as 100,000 IOPS, up to 500 GiB/s throughput. | As much as 460,000 IOPS. As much as 36 GiB/s throughput. |
-| **Scale** | As much as 2 PiB for a single volume. <br><br> As much as ~4.75 TiB for a single file. <br><br> No minimum capacity requirements. | As much as 100 TiB for a single volume. <br><br> As much as 4 TiB for a single file. <br><br> 100 GiB minimum capacity. | As much as 2.5 PiB for a single volume. <br><br> As much as 32 PB for a single file. <br><br> 4 TiB minimum capacity. | As much as 100 TiB for a single volume. <br><br> As much as 16 TiB for a single file. <br><br> Consistent hybrid cloud experience. |
+| **Key features** | Integrated with Azure HPC Cache for low-latency workloads. <br><br> Integrated management, including lifecycle management, immutable blobs, data failover, and metadata index. | Zonally redundant for high availability. <br><br> Consistent single-digit millisecond latency. <br><br> Predictable performance and cost that scales with capacity. | High storage capacity up to 12.5 PiB upon request. <br><br> Low (~2ms) latency. <br><br> Spin up new clusters in minutes. <br><br> Supports containerized workloads with AKS. | Extremely low latency (as low as sub-millisecond). <br><br> Rich NetApp ONTAP management capability, like SnapMirror Cloud. <br><br> Consistent hybrid cloud experience. |
+| **Performance (per volume)** | As much as 20,000 IOPS. As much as 100 GiB/s throughput. | As much as 100,000 IOPS. As much as 80 GiB/s throughput. | As much as 1M IOPS, up to 500 GiB/s throughput. | As much as 460,000 IOPS. As much as 36 GiB/s throughput. |
+| **Scale** | As much as 2 PiB for a single volume. <br><br> As much as ~4.75 TiB for a single file. <br><br> No minimum capacity requirements. | As much as 100 TiB for a single volume. <br><br> As much as 4 TiB for a single file. <br><br> 100 GiB minimum capacity. | Up to 12.5 PiB upon request for a single volume. <br><br> As much as 31.25 PiB for a single file. <br><br> 4 TiB minimum capacity. | As much as 100 TiB for a single volume. <br><br> As much as 16 TiB for a single file. <br><br> Consistent hybrid cloud experience. |
 | **Pricing** | [Azure Blob Storage pricing](https://azure.microsoft.com/pricing/details/storage/blobs) | [Azure Files pricing](https://azure.microsoft.com/pricing/details/storage/files) | [Azure Managed Lustre pricing](https://azure.microsoft.com/pricing/details/managed-lustre) | [Azure NetApp Files pricing](https://azure.microsoft.com/pricing/details/netapp) |
 
-
 ## Next steps
 
 The following articles provide guidance that you might find helpful at various points during your cloud adoption process. They can help you succeed in your cloud adoption scenario for HPC in the finance sector.