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LLMOps and CI/CD on Azure for GenAI Apps (Azure Gen AI Series, Part 28)
An Azure LLMOps pipeline looks like MLOps until the gate. Here is how to block a merge on an evaluation score, register the flow, and roll it out blue to green on a managed online endpoint, plus why new pipelines should skip Prompt Flow.
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Azure Responsible AI, the RAI Dashboard and Scorecard (Azure Gen AI Series, Part 27)
Responsible AI on Azure is two toolchains: the Responsible AI dashboard for tabular models and Foundry safety evaluators for generative apps. Here is which one your workload needs, how to run each, and how to keep a scorecard for the EU AI Act.
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Azure OpenAI Cost Governance and FinOps (Azure Gen AI Series, Part 26)
When a Provisioned Throughput reservation actually beats pay as you go on Azure OpenAI, how to read a bill hidden under Cognitive Services, and the Batch and caching discounts you get for free.
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Azure Monitor Observability for GenAI, from Metrics to Traces (Azure Gen AI Series, Part 25)
Azure GenAI observability comes in three layers: free platform metrics, per request diagnostic logs in Log Analytics, and OpenTelemetry traces in Application Insights. Here is what each one sees, what it costs, and the order I turn them on.
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Vision, Audio, Image, and Document Models on Azure OpenAI (Azure Gen AI Series, Part 24)
Multimodal on Azure is not one switch, it is four services. Where vision, voice, image generation, and Content Understanding each live, what they cost, and which one owns each input.
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Azure AI Foundry Evaluation and Observability, from CI Gate to Live Traffic (Azure Gen AI Series, Part 23)
Evaluation scores catch a bad agent; tracing tells you why it went bad. Here is how Microsoft Foundry runs the same evaluators at dev time, in your CI gate, and against live traffic, and what continuous evaluation actually costs.
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Microsoft Copilot Studio, from First Agent to Autonomous Workflow (Azure Gen AI Series, Part 22)
Copilot Studio builds low-code agents on Microsoft 365 and Power Platform. Where building is free, where Copilot Credits start, and when to reach for pro-code instead.
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Semantic Kernel, AutoGen, and Microsoft Agent Framework on Azure (Azure Gen AI Series, Part 21)
Semantic Kernel and AutoGen merged into Microsoft Agent Framework in 2026. Here is how the unified SDK works, what a migration costs, and where I would still wait.
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Distributed Training on Azure ML with ND GPU Clusters (Azure Gen AI Series, Part 19)
How to spread one training run across Azure ND H100 clusters with PyTorch, NCCL, and InfiniBand, when adding nodes pays off, and how to keep scaling efficiency from collapsing.
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Deploy Open Models on Azure Machine Learning with Managed Compute (Azure Gen AI Series, Part 18)
Open models on Azure Machine Learning run on managed compute, dedicated GPU VMs you rent by the hour instead of paying per token. Here is when that trade beats serverless, how to size the SKU, and where the community registry leaves you on your own.
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Azure OpenAI Distillation and Stored Completions (Azure Gen AI Series, Part 17)
Capture production traffic with store=True, then distill a small Azure OpenAI model that answers like a flagship. The workflow, the real costs, and the traffic volume where it pays off.
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