Merge pull request #314246 from cdpark/foundry-branding-training

Foundry Branding - content only

Author: prmerger-automator[bot]

articles/high-performance-computing/performance-benchmarking/platform-selection-best-practices-for-hpc-ai-models.md

@@ -14,7 +14,7 @@ ms.date: 02/12/2026
 
 Azure provides multiple platforms for training, fine-tuning, and deploying AI models. Each platform addresses different requirements such as managed fine-tuning of foundation models, full MLOps lifecycle management, HPC-style training with Slurm, or Kubernetes-native portability.
 
-While the Azure Well-Architected Framework (WAF) commonly recommends **Azure Machine Learning** for managed training and MLOps. Other platforms such as **Azure AI Foundry**, **Azure Kubernetes Service (AKS)**, and **Azure CycleCloud** are also valid and recommended depending on the scenario.
+While the Azure Well-Architected Framework (WAF) commonly recommends **Azure Machine Learning** for managed training and MLOps. Other platforms such as **Microsoft Foundry**, **Azure Kubernetes Service (AKS)**, and **Azure CycleCloud** are also valid and recommended depending on the scenario.
 
 The platforms overlap in capability, which can create confusion. This guide focuses on decision clarity rather than prescribing a single correct platform and helps choose the appropriate Azure platform for AI workloads based on workload type, operational model, and customer requirements.
 
@@ -24,11 +24,11 @@ The platforms overlap in capability, which can create confusion. This guide focu
 
 :::image type="content" source="../media/ai-training-platform-decision-guide.png" alt-text="Diagram depicting platform decision tree.":::
 
-### Azure AI Foundry
+### Microsoft Foundry
 
-Choose Azure AI Foundry when you need a managed, end-to-end platform to fine-tune foundation models such as GPT, Llama, or Phi and deploy them safely at scale. 
+Choose Microsoft Foundry when you need a managed, end-to-end platform to fine-tune foundation models such as GPT, Llama, or Phi and deploy them safely at scale. 
 
-Azure AI Foundry simplifies fine-tuning without infrastructure management, provides integrated content safety and responsible AI controls, enables faster AI app development through visual prompt flow orchestration. It offers unified access to Azure OpenAI models with customization across the model lifecycle. 
+Foundry simplifies fine-tuning without infrastructure management, provides integrated content safety and responsible AI controls, enables faster AI app development through visual prompt flow orchestration. It offers unified access to Azure OpenAI models with customization across the model lifecycle. 
 
 Consider alternatives like Azure Machine Learning for custom training loops or unsupported models, and CycleCloud for Slurm-based workflows.
 
@@ -40,7 +40,7 @@ It supports experiment tracking for reproducibility, automated training and depl
 
 Azure ML also provides robust model versioning, registration, and governance through its model registry, supporting compliance and audit needs, while offering managed compute so data scientists can focus on modeling rather than infrastructure. It can also attach AKS clusters as compute targets, combining Kubernetes-based training with Azure ML’s orchestration and tracking. 
 
-For simpler foundation model fine-tuning, Azure AI Foundry may be a better fit, while CycleCloud suits Slurm/PBS scheduler needs and AKS is preferred for multi-cloud portability.
+For simpler foundation model fine-tuning, Microsoft Foundry may be a better fit, while CycleCloud suits Slurm/PBS scheduler needs and AKS is preferred for multi-cloud portability.
 
 
 ### Azure CycleCloud Workspace for Slurm
@@ -66,7 +66,7 @@ Consider alternatives if you want integrated experiment tracking and MLOps pipel
 
 The following table compares Azure platforms commonly used for AI training and deployment, highlighting how each aligns with different workload requirements, operational models, and infrastructure preferences.
 
-| Capability | AI Foundry | Azure ML | CycleCloud | AKS |
+| Capability | Foundry | Azure ML | CycleCloud | AKS |
 |------------|:----------:|:--------:|:----------:|:---:|
 | Foundation model fine-tuning | Built-in, managed | Supported | Requires custom setup | Requires custom setup |
 | Custom model training | Supported (scoped scearios) | Native supported | Fully supported | Fully supported |
@@ -81,7 +81,7 @@ The following table compares Azure platforms commonly used for AI training and d
 
 **Enterprise foundation model tuning and deployment**
 
-For scenarios focused on fine‑tuning and deploying foundation models, Azure AI Foundry provides an end‑to‑end managed experience. It supports a streamlined path from model customization to deployment, with built‑in safety and governance features that reduce operational complexity for enterprise LLM applications.
+For scenarios focused on fine‑tuning and deploying foundation models, Foundry provides an end‑to‑end managed experience. It supports a streamlined path from model customization to deployment, with built‑in safety and governance features that reduce operational complexity for enterprise LLM applications.
 
 **Custom machine learning with full MLOps**
 
@@ -104,7 +104,7 @@ Organizations prioritizing portability and platform consistency across environme
 
 For complex implementations and enterprise architectures, you can combine platforms.
 
-- Enterprise LLM applications benefit from an end‑to‑end managed approach using Azure AI Foundry for both fine‑tuning and deployment, simplifying the model lifecycle and reducing operational overhead.
+- Enterprise LLM applications benefit from an end‑to‑end managed approach using Foundry for both fine‑tuning and deployment, simplifying the model lifecycle and reducing operational overhead.
 
 - For custom machine learning workloads that require Kubernetes-based serving, Azure Machine Learning handles training and MLOps while AKS provides scalable, production‑grade inference.