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Azure Foundry Model Catalog and Models-as-a-Service (Azure Gen AI Series, Part 4)
The Foundry catalog splits into models sold by Azure and partner models, and every one deploys as serverless Models-as-a-Service or as managed compute. Here is how to pick, with the permission and rate-limit traps that bite first.
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Azure OpenAI in Foundry Models, and How to Choose One (Azure Gen AI Series, Part 3)
Azure OpenAI in Foundry Models is the set of OpenAI models you deploy inside a Foundry resource. Here is which models you get in 2026, how to pick one, and how the call goes out with the v1 API.
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Azure AI Foundry Platform and Foundry Projects Explained (Azure Gen AI Series, Part 2)
Microsoft renamed Azure AI Foundry and rebuilt it around one resource, projects, and a single SDK. Here is what the platform is, how projects work, and your first API call against it.
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Azure Generative AI Stack, End to End (Azure Gen AI Series, Part 1)
The whole Azure generative AI stack in one map: Azure AI Foundry, the model catalog, the three deployment types that decide your bill, retrieval, agents, and the compute underneath. Where to start and what to skip.
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AWS Generative AI vs the Field, the Verdict (AWS Gen AI Series, Part 30)
After thirty parts on the AWS generative AI stack, the verdict: where Bedrock, Amazon Nova, and AWS silicon earn the default, and the exact cases where Azure, Google Cloud, or IBM watsonx fit better.
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Bedrock Reference Architectures for Chatbot, RAG, Agentic, and Batch (AWS Gen AI Series, Part 29)
Most AWS generative AI features are one of four shapes: chatbot, RAG, agentic, or batch. Here is how each maps to Amazon Bedrock services, what it costs, and which one to reach for first.
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LLMOps and CI/CD for Amazon Bedrock and SageMaker (AWS Gen AI Series, Part 28)
LLMOps on AWS is two pipelines, not one. Version Bedrock prompts as code, gate every change on evaluation, register and deploy models through SageMaker, and keep a rollback you have actually tested.
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Responsible AI and Watermark Detection on Amazon Bedrock (AWS Gen AI Series, Part 27)
A practical walk through responsible AI on AWS: the eight dimensions AWS documents, invisible watermarking on Titan and Nova, the DetectGeneratedContent API, and bias checks with SageMaker Clarify.
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Amazon Bedrock Cost Governance and FinOps on AWS (AWS Gen AI Series, Part 26)
Bedrock cost is driven by tokens, model choice, and inference tier. Here is how I attribute spend by team, cut it with batch and prompt caching, and put budgets and anomaly alerts around it before the bill surprises anyone.
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Amazon Bedrock Observability with CloudWatch and Invocation Logging (AWS Gen AI Series, Part 25)
Bedrock ships almost no history by default. Here is how I turn on model invocation logging, pick the CloudWatch metrics worth an alarm, and pull token cost per model straight from the logs.
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Amazon Bedrock Data Automation for Multimodal Content (AWS Gen AI Series, Part 24)
A practical walk through Amazon Bedrock Data Automation: standard output versus custom blueprints, the async API, real per-page and per-minute pricing, and when to wire it into a Knowledge Base.
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Amazon Bedrock Model Evaluation, Automatic to LLM-as-a-Judge (AWS Gen AI Series, Part 23)
Amazon Bedrock has three ways to score a model: programmatic metrics, an LLM judge, and human review. Here is what each measures, what it costs, and how to run a judge job without fooling yourself.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha