From Traction to Production: Maturing your LLMOps step by step

Is your LLM app stuck in the prototype phase? Learn the four-stage maturity model to systematically advance your project to production-ready excellence.

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Understanding the business motivation for adopting AI solutions

AI investments show a significant return on investment, typically yielding three to five dollars back for every dollar spent within about 14 months.

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Overcoming the common challenges in generative AI adoption

Key obstacles to adopting generative AI include the rapid pace of innovation, the need for specialized expertise, data integration complexity, and difficulties in evaluation and operationalization.

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Defining LLMOps and understanding its core benefits

LLMOps is a specialized discipline, similar to DevOps, that combines people, processes, and platforms to automate and manage the lifecycle of LLM-infused applications.

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Differentiating between LLMOps and traditional MLOps

LLMOps focuses on application developers and assets like prompts and APIs, whereas MLOps is geared towards data scientists and focuses on building and training models from scratch.

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Exploring the complete lifecycle of an LLM application

The LLM application lifecycle involves iterative cycles of ideation, building with prompt engineering and RAG, and operationalization, all governed by security and compliance.

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Navigating the four stages of the LLMOps maturity model

The LLMOps maturity model progresses from an initial, manual stage to developing, managed, and finally an optimized stage with full automation and continuous improvement.

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Introducing the Azure AI platform for end-to-end LLMOps

Azure AI provides a comprehensive suite of tools, including the Azure AI Foundry, to support the entire LLM lifecycle from model selection to deployment and governance.

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Using Azure AI for model selection and benchmarking

The Azure AI model catalog offers over 1,800 models and includes powerful benchmarking tools to compare them based on quality, cost, latency, and throughput.

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Building applications with RAG and Azure Prompt Flow

Azure AI Search facilitates retrieval-augmented generation (RAG), while the open-source Prompt Flow framework helps orchestrate, evaluate, and manage complex LLM workflows.

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Deploying and monitoring flows with Azure AI tools

Azure AI enables the deployment of Prompt Flow workflows as scalable endpoints and includes tools for fine-tuning, content safety filtering, and comprehensive monitoring of cost and performance.

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How to assess and advance your LLMOps maturity

To mature your LLMOps practices, start by assessing your current stage, understanding the application lifecycle, and selecting the right tools like Azure AI Foundry.