Wednesday, July 24, 2024

Large language model operations Success Stories

AI has repeatedly accelerated business growth by improving operations, personalising customer interactions, and launching new goods and services. Generative AI and foundation model shifts in the last year are accelerating AI adoption in organisations as they see Azure OpenAI Service’s potential. They also recommend new tools, methods, and a fundamental change in how technical and non-technical teams work to grow AI practises. 

Large language model operations (LLMOps) describe this change. Azure AI has several features to support healthy LLMOps before the name was coined, drawing on its MLOps platform roots. But at our Build event last spring, Microsoft introduced prompt flow, a new capability in Azure AI that raises the bar for LLMOps. Last month, Microsoft released the public preview of prompt flow’s code-first experience in the Azure AI Software Development Kit, Command Line Interface, and VS Code extension. 

LLMOps and Azure AI in particular will be discussed in greater detail today. Microsoft launched this new blog series on LLMOps for foundation models to share our learnings with the industry and explore its implications for organisations worldwide. The series will explore what makes generative AI distinctive, how it may solve business problems, and how it encourages new types of teamwork to build the next generation of apps and services. The series will also teach organisations safe AI practises and data governance as they develop today and in the future. 

From MLOps to LLMOps

The latest foundation model is often the focus, but building systems that use LLMs requires selecting the right models, designing architecture, orchestrating prompts, embedding them into applications, checking them for groundedness, and monitoring them with responsible AI toolchains. Customers who started with MLOps will realise that MLOps practises prepare them for LLMOps. 

LLMs are non-deterministic, so Microsoft must work with them differently than typical ML models. A data scientist today may define weights, regulate training and testing data, spot biases using the Azure Machine Learning responsible AI dashboard, and monitor the model in production. 

The best practises for modern LLM-based systems include quick engineering, evaluation, data grounding, vector search configuration, chunking, embedding, safety mechanisms, and testing/evaluation. 

Like MLOps, LLMOps goes beyond technology and product adoption. It’s a mix of problem-solvers, processes, and products. Compliance, legal, and subject matter experts commonly work with data science, user experience design, and engineering teams to put LLMs to production. As the system grows, the team needs to be ready to think through often complex questions about topics such as how to deal with the variance you might see in model output, or how best to tackle a safety issue.

Overcoming LLM-Powered app development issues

An LLM-based application system has three phases:

  • Startup or initialization: You choose your business use case and quickly launch a proof of concept. This step includes choosing the user experience, data to pull into it (e.g., retrieval enhanced generation), and business questions concerning impact. To start, create an Azure AI Search index on data and utilise the user interface to add data to a model like GPT 4 to construct an endpoint.
  • Evaluation and Refinement: After the Proof of Concept, experiment with meta prompts, data indexing methods, and models. Prompt flow lets you construct flows and experiments, run them against sample data, evaluate their performance, and iterate if needed. Test the flow on a larger dataset, evaluate the results, and make any necessary changes. If the results meet expectations, continue.
  • Production: After testing, you deploy the system using DevOps and use Azure AI to monitor its performance in production and collect usage data and feedback. This data is used to improve flow and contribute to early stages for future iterations.

Microsoft strives to improve Azure’s reliability, privacy, security, inclusiveness, and correctness. Identifying, quantifying, and minimising generative AI harms is our top priority. With powerful natural language processing (NLP) content and code generating capabilities through (LLMs) like Llama 2 and GPT-4, Microsoft have created specific mitigations to assure responsible solutions. Microsoft streamline LLMOps and improve operational preparedness by preventing errors before application production.

Responsible AI requires monitoring findings for biases, misleading or inaccurate information, and addressing data groundedness concerns throughout the process. Prompt flow and Azure AI Content Safety help, but application developers and data scientists shoulder most of the responsibilities.

Design-test-revise during production can improve your application.

Azure accelerates innovation for companies

Microsoft has spent the last decade studying how organisations use developer and data scientist toolchains to build and expand applications and models. Recently, their work with customers and creating their Copilots has taught us a lot about the model lifecycle and helped them us streamline LLMOps’ workflow with Azure AI capabilities. 

LLMOps relies on an orchestration layer to connect user inputs to models for precise, context-aware answers. 

The quick flow feature of LLMOps on Azure is notable. This makes LLMs scalable and orchestrable, managing multiple prompt patterns precisely. Version control, flawless continuous integration, continuous delivery integration, and LLM asset monitoring are ensured. These traits improve LLM pipeline reproducibility and encourage machine learning engineers, app developers, and prompt engineers to collaborate. It helps developers produce consistent experiment results and performance.

Data processing is essential to LLMOps. Azure AI Integration is optimised to operate with Azure data sources such vector indices like Azure AI Search, databases like Microsoft Fabric, Azure Data Lake Storage Gen2, and Azure Blob Storage. This integration makes data access easy for developers, who can use it to improve LLMs or customise them.

Azure AI has a large model catalogue of foundation models, including Meta’s Llama 2, Falcon, and Stable Diffusion, in addition to OpenAI frontier models like GPT-4 and DALL-E. Customers can fast-start with little friction by employing pre-trained models from the model catalogue to reduce development time and computing costs. Developers can customise, evaluate, and deploy commercial apps confidently with Azure’s end-to-end security with unmatched scalability and comprehensive model selection.

Present and future LLMOps

Microsoft provides certification courses, tutorials, and training to help you succeed with Azure. Our application development, cloud migration, generative AI, and LLMOps courses are updated to reflect prompt engineering, fine-tuning, and LLM app development trends. 

However, invention continues. Vision Models were added to Azure AI model catalogue recently. Now, Azure’s vast catalogue offers a variety of curated models to the community. Vision provides image classification, object segmentation, and object detection models tested across architectures and delivered with default hyperparameters for reliable performance.

Microsoft will continue to enhance their product portfolio before their annual Microsoft Ignite Conference next month.

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