Azure examined Large Language Models (LLMs) and their responsible use in AI operations in its LLMOps blog series. They introduce the LLMOps maturity model, a crucial guide for business leaders. This model is a strategic guide that shows why understanding and implementing this model is crucial for navigating the ever-changing AI landscape. It covers everything from foundational LLM use to deployment and operational management. Siemens uses Microsoft Azure AI Studio and prompt flow to streamline LLM workflows for their industry-leading product lifecycle management (PLM) solution Teamcenter and connect problem-solvers with solution providers. This real-world application shows how the LLMOps maturity model helps transform AI potential into impactful deployment in complex industries.
Exploring Azure application and operational maturity
The multifaceted LLMOps maturity model captures two crucial aspects of working with LLMs: application development sophistication and operational process maturity.
Application maturity: LLM techniques improvement within an application. Start by exploring LLM’s broad capabilities, then move on to fine-tuning and Retrieval Augmented Generation (RAG) to meet specific needs.
Scaling applications requires operational maturity, regardless of LLM technique complexity. Methodical deployment, monitoring, and maintenance are included. The goal is to make LLM applications reliable, scalable, and maintainable regardless of complexity.
This maturity model reflects the dynamic and ever-changing LLM technoolgy landscape, which requires flexibility and methodical approach. The field’s constant advancement and exploration require this balance. Each level of the model has its own rationale and progression strategy, giving organizations a clear roadmap to improve LLM.
LLMOps Maturity Model
Exploration begins with Level One
Organizations discover and understand at this foundational stage. Exploring pre-built LLMs like Microsoft Azure OpenAI Service APIs or Models as a Service (MaaS) inference APIs is the main focus. This phase requires basic coding skills to interact with APIs, understand their functions, and try simple prompts. Manual processes and isolated experiments characterize this level, which doesn’t prioritize assessment, monitoring, or advanced deployment strategies. Instead, the main goal is to experiment with LLMs to understand their potential and limitations and apply them to real-world situations.
Developers at Contoso are encouraged to try GPT-4 from Azure OpenAI Service and LLama 2 from Meta AI. They can use the Azure AI model catalog to find the best models for their datasets. The foundation for advanced applications and operational strategies in LLMOps is laid here.
Second Level: Systematizing LLM app development
More proficient LLM users adopt a systematic approach to operations. Prompt design and use of meta prompt templates in Azure AI Studio are covered in this level of structured development. Developers learn how prompts affect LLM outputs and the importance of responsible AI in generated content at this level.
Azure AI prompt flow helps here. It simplifies prototyping, experimenting, iterating, and deploying LLM-powered AI applications by streamlining the entire development cycle. Developers begin responsibly evaluating and monitoring LLM flows. Developers can evaluate applications on accuracy and responsible AI metrics like groundedness using prompt flow. Integrating LLMs with RAG techniques to pull information from organizational data allows for tailored LLM solutions that maintain data relevance and optimize costs.
AI developers at Contoso use Azure AI Search to index vector databases. RAG with prompt flow incorporates these indexes into prompts to provide more contextual, grounded, and relevant responses. This stage moves from basic exploration to focused experimentation to understand how LLMs can solve specific problems.
Managed Level Three: Advanced LLM workflows and proactive monitoring
Developers refine prompt engineering to create more complex prompts and integrate them into applications. This requires understanding how prompts affect LLM behavior and outputs to create more tailored and effective AI solutions.
Developers use prompt flow’s plugins and function callings to create complex LLM flows at this level. They can track changes and rollback to previous versions of prompts, code, configurations, and environments using code repositories. By running batch runs using relevance, groundedness, and similarity metrics, prompt flow’s iterative evaluation capabilities refine LLM flows. This lets them build and compare metaprompt variations to find those that produce higher-quality outputs that meet their business goals and responsible AI guidelines.
Additionally, flow deployment is more systematic in this stage. Companies automate deployment pipelines and use CI/CD. Automation improves LLM application deployment efficiency and reliability, indicating a shift toward maturity.
In this stage, monitoring and maintenance develop. Developers monitor metrics to ensure reliable operations. These include groundedness, similarity, latency, error rate, token consumption, and content safety metrics.
Developers in Contoso create different Azure AI prompt flow variations to improve accuracy and relevance. They continuously evaluate their LLM flows using advanced metrics like QnA Groundedness and QnA Relevance during batch runs. After reviewing these flows, they package and automate deployment with the prompt flow SDK and CLI, integrating with CI/CD processes. Contoso also updates Azure AI Search to create more complex and efficient vector database indexes using RAG. This makes LLM applications faster, more contextually informed, and cheaper, reducing operational costs and improving performance.
Level Four: Optimised operations and improvement
At the top of the LLMOps maturity model, organizations prioritize operational excellence and continuous improvement. Monitoring and iterative improvement accompany sophisticated deployment processes in this phase. Advanced monitoring solutions provide deep LLM application insights, enabling dynamic model and process improvement.
Contoso developers perform complex prompt engineering and model optimization at this stage. They develop reliable and efficient LLM applications using Azure AI’s extensive toolkit. They optimize GPT-4, Llama 2, and Falcon models for specific needs and set up complex RAG patterns to improve query understanding and retrieval, making LLM outputs more logical and relevant. Their large-scale evaluations of LLM applications using sophisticated metrics for quality, cost, and latency ensure thorough evaluation. A LLM-powered simulator can generate conversational datasets for developers to test and improve accuracy and groundedness. Evaluations at various stages foster a culture of continuous improvement.
To monitor and maintain, Contoso uses predictive analytics, detailed query and response logging, and tracing. These methods enhance prompts, RAG implementations, and fine-tuning. Their LLM applications meet industry and ethical standards by using A/B testing and automated alerts to detect drifts, biases, and quality issues.
The deployment process is efficient at this point. Contoso manages LLMOps application lifecycles, including versioning and predefined auto-approval. They always use advanced CI/CD practices with robust rollback capabilities to update LLM applications smoothly.
At this stage, Contoso is a model of LLMOps maturity, demonstrating operational excellence and a commitment to LLM innovation and improvement.
Identify your journey stage
Each LLMOps maturity model level is a strategic step toward production-level LLM applications. The field is dynamic as it evolves from basic understanding to advanced integration and optimization. It recognized the need for continuous learning and adaptation to help organizations sustainably use LLMs’ transformative power.
Organizations can navigate LLM application implementation and scaling with the LLMOps maturity model. Organizations can make better model progression decisions by distinguishing application sophistication from operational maturity. The introduction of Azure AI Studio, which integrated prompt flow, model catalog, and Azure AI Search, emphasizes the importance of cutting-edge technology and robust operational strategies in LLM success.
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