Tag: LLMOps
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watsonx LLMOps on OpenShift, from Notebook to Promotion Gate (IBM Gen AI Series, Part 22)
How to run generative AI features in production on the IBM stack: watsonx.ai for models and governance, Red Hat OpenShift AI for pipelines and GPUs, and a promotion gate that refuses to ship a regression.
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Vertex AI Pipelines for LLMOps, from Notebook to Nightly Retrain (Google Cloud Gen AI Series, Part 28)
How I turn a Gemini tuning notebook into a Vertex AI pipeline that reruns nightly, gates on evals, registers versions, and does not quietly ship a worse model.
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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 Prompt Flow, Authoring to Evaluation to Deploy (Azure Gen AI Series, Part 15)
Prompt Flow now carries a retirement date. Here is how it works, when it still earns a place in your Azure pipeline, and where to build new instead.
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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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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.
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