LLMOps: why, what and how with Azure Machine Learning Prompt Flow

This is part 1 of 2 of our LLMOps Guide: in the next part we apply LLMOps to build a Knowledge Mining solution

On the 15th of November, Azure Machine Learning Prompt Flow has been made available to the general public, marking a significant milestone in the realm of Azure AI. This release represents the next step in empowering engineers and data scientists to create high-quality generative AI applications.

Building high quality LLM apps is build on two foundations: augmenting and operationalising.

Introduction

Prompt Flow initially emerged as a feature within Azure Machine Learning, with the primary goal of simplifying and streamlining the prompt engineering process.

In this article, we’ll dive deeper into the evolution of Prompt Flow, highlighting the enhancements that make it an indispensable tool for developers. We’ll explore how Prompt Flow enables prompt tuning, evaluation, experimentation, orchestration and LLMOps for automation.

Automating LLM Application Development with Code and LLMOps

Developing Large Language Model (LLM) applications is complex and benefits greatly from a strong development environment. Azure Machine Learning Prompt Flow uses a code-first approach, treating assets as files and promoting version control and collaboration with source code management tools. This is essential for integrating with LLMOps, which follows DevOps principles to make the development-to-deployment process more efficient.

LLMOps focuses on automation, ongoing monitoring, evaluation, and collaboration among development, data science, and operations teams. This method is key in reducing the time from development to deployment and ensuring the quality and reliability of LLM applications.

A template

In the quest to accelerate LLM application development, we use the LLMOps solution accelerator template. The template allows us to simplify the following 3 concepts:

• Preconfigured Workflows: Featuring preconfigured workflows (Azure DevOps pipelines or GitHub actions) for prompt engineering, evaluation, and deployment, reducing setup time substantially.
• Customization: Tailor the template to align with your specific requirements, organizational goals, and workflow preferences.
• Integration: The template integrates with the Prompt Flow SDK/CLI, presenting a comprehensive solution for efficient LLM application development and deployment.

A high level overview of the Accelerator Template

Evaluation strategy

To create high-quality Large Language Models , a detailed evaluation strategy is crucial, with Azure Machine Learning Prompt Flow being a key component. Prompt Flow supports several aspects of this process:

1. Evaluation Data Management: It helps in preparing, generating, and managing evaluation data effectively.
2. Evaluation Metrics: It uses various metrics like helpfulness, honesty, harmlessness, performance, and cost to assess different aspects of LLM applications.
3. Evaluation Methods and Visualization: Prompt Flow employs various methods and visualization techniques for comparing results and extracting insights.
4. Evaluation Journey: It involves evaluation at different stages such as during development, before deployment (Continuous Integration), and after deployment (Continuous Evaluation) to ensure the applications are helpful and robust.
5. User Feedback Loop: It constantly integrates user feedback to enhance LLM applications.
6. Automation via LLMOps: The evaluation process is automated for improved efficiency and accuracy.

To create high-quality Large Language Models , a detailed evaluation strategy is crucial

Semantic Kernel Meets Prompt Flow

Semantic Kernel, an open-source SDK, enhances AI model and plugin orchestration, leading to advanced LLM applications. Its integration with Prompt Flow combines solid infrastructure with smart capabilities, enabling smooth evaluation and deployment of planners and plugins. Developers can also use deployed flow endpoints from Prompt Flow as plugins in Semantic Kernel for better orchestration. The main advantages are twofold:

• Automated Evaluation and Deployment: Prompt Flow automates the testing and evaluation of planners and plugins from Semantic Kernel. It allows for the creation of new flows, batch testing, and accurate measurement of planner and plugin effectiveness. Prompt Flow also improves planner quality through prompt engineering.
• Endpoint Deployment for Advanced Orchestration: Developers can develop, evaluate, and deploy flows with Prompt Flow and integrate these flows as plugins in Semantic Kernel. This facilitates the creation of LLM applications with complex orchestration, utilizing Prompt Flow’s evaluation and deployment features along with Semantic Kernel’s orchestration capabilities.

Furthermore, Prompt Flow is compatible with other LLM application frameworks, offering a versatile platform for evaluation, deployment, and monitoring. This expands the possibilities for LLM application development and management

MLLops envisioned by DALLE-3

Conclusion

We’ve explored the capabilities of Prompt Flow, offering developers an integrated ecosystem to build sophisticated, knowledge-driven LLM applications with ease. From automated evaluation and deployment to advanced orchestration capabilities, this integration opens up exciting possibilities for creating high-quality LLM applications efficiently.

Are you ready to take your LLM application development to the next level? Don’t hesitate to reach out to us at Model8 and learn more!