Chapter 14: Arrival: Deployment Strategies

In this chapter

As the landscape of AI-powered solutions transitions from being experimental to being a practical tool for customers, the strategies for deploying AI become increasingly more significant. Understanding how to efficiently deploy and manage these technologies on Azure is not just beneficial but necessary for staying competitive. This chapter delves into the ever-changing landscape of AI on Azure highlighting the importance of efficient, scalable, and secure deployment methods to maximize the success of AI applications with a global reach. It builds on the understanding of previous chapters on design, development, testing, and observability to provide a comprehensive guide to the approaches for ensuring consistent delivery of AI solutions on Azure.

Arrival: Deployment Strategies

Foundations of AI Deployment on Azure

The challenge in bringing AI solutions on Azure to production lies in the complexity of the deployment and configuration of environments. The need for a reliable, automated process to ensure a consistent deployment of AI solutions is critical to their success.

Engineering teams building AI solutions on Azure must consider the following foundations of consistent deployment:

  • DevOps: DevOps is a set of practices that combines software development and IT operations. It aims to shorten an AI solution’s development lifecycle and provide continuous delivery with high quality. DevOps focuses on the automation, testing, monitoring, and continuous delivery of your AI solution.

  • MLOps: Building on top of DevOps practices, MLOps provides a set of practices for collaboration and communication between data scientists and operations professionals to help manage the production of machine learning models. MLOps establishes a consistent and reliable process for building, training, and deploying ML models to production.

  • LLMOps: LLMOps builds on the practices of MLOps for teams building solutions using pre-trained LLMs, such as OpenAI’s GPT models. LLMOps establishes practices for exploring LLMs, implementing prompt engineering, and fine tuning to validate, deploy, and monitor LLMs in production.

Adopting LLMOps for Azure AI solutions

Large language models (LLMs) are powerful tools for AI solutions that generate natural language for various applications, such as chatbots, summarization, and content creation. However, evaluating, deploying and managing LLMs in a production solution is not a trivial task. It requires careful consideration of the challenges, risks, and best practices associated with LLMs. This is where LLMOps comes in.

An introduction to LLMOps provides a comprehensive guide on how to adopt LLMOps for your AI solutions using Azure’s AI tooling. You will learn how to:

  • Use the model catalog to explore LLMs: The Azure AI Studio provides a model catalog that is carefully curated by Microsoft to help you discover language models from the open-source community, as well as OpenAI’s GPT models. Use the model catalog to explore LLMs and find the best model for your use case.

  • Curate data for fine-tuning: Although LLMs are pre-trained on large datasets with the ability to perform a wide range of tasks, they may require you to fine-tune them on your own data to improve the accuracy of results. Use fine-tuning when you have a specific use case that is proving to be a challenging domain for a model. Avoid prematurely fine-tuning a model without first evaluating its performance.

  • Evaluate prompts with Prompt Flow: Any AI solution taking advantage of LLMs are based on prompts that instruct the model to perform tasks. Prompt engineering is a critical step in the development of AI solutions using LLMs and requires careful consideration of the prompts used to generate the desired results. Use prompt flow throughout your AI solution’s development lifecycle to evaluate prompts and ensure the model is generating the desired results.

  • Deploy and monitor your LLM: Once you have evaluated and fine-tuned your LLM, you are ready to deploy it to production. Use the Azure AI Studio to deploy your LLM as an API you can use for inference. Continuously monitor the performance of your LLM deployment to ensure it continues to generate the desired results for your AI solution use cases.

Taking advantage of the tooling in the Azure AI Studio and adopting LLMOps practices will help you to efficiently and effectively deploy end-to-end AI solutions using LLMs.

Using Azure Machine Learning for managing end-to-end model lifecycle

MLOps offers crucial practices that help teams building custom machine learning models for their AI solutions to build, manage and deploy them in a scalable and reliable way. Teams adopting MLOps improve their machine learning lifecycle and deliver value to their customers faster and more efficiently.

Azure Machine Learning provides a comprehensive suite of tools that provide the end-to-end implementation of MLOps practices for building, training, and deploying your models to production at scale. To safely roll out your models to production in Azure Machine Learning, consider the following practices:

  • Version models: Similar to versioning your software, versioning your models is a critical practice for tracking the changes to your models over time. It allows you to easily deploy, upgrade, and roll back your model with a clear understanding of the changes made to it.

  • Deploy safely: Versioning provides multiple benefits including the ability to deploy multiple versions of a model in a blue-green deployment for A/B testing. Use this approach to provide a consistent and reliable experience for your customers when rolling out new or experimental versions of your models for testing safely.

  • Monitor and retrain: Once your model is deployed to production, you must continuously monitor its performance to ensure it is generating reliable results. Addressing model drift identified by monitoring is critical for the successful deployment and longevity of machine learning models in production.

By adopting these practices, you can ensure that your machine learning models are deployed safely and reliably to production. This will help you to deliver value to your customers faster and more efficiently.

Key Takeaways and Actionable Strategies for AI Implementation

The effective deployment of AI solutions has become an essential skill for engineering teams to master as AI transitions from experimental to practical applications. Adopting the practices and strategies outlined in this chapter is crucial for maintaining competitiveness and ensuring the success of AI applications on a global scale. The integration of DevOps, MLOps, and LLMOps into deployment processes enhances the reliability, automation, and consistency of AI solutions. To help CTOs, Development Leads, and Chief Architects in adopting these practices, here are key recommendations:

  • Integrate DevOps into the AI Lifecycle: Leverage Azure’s tools and best practices to streamline the development, deployment, and monitoring of AI solutions. This includes automating processes, continuously testing, and monitoring performance to ensure quality and efficiency.

  • Adopt LLMOps for solutions using Large Language Models (LLMs): Utilize Azure AI Studio’s model catalog to explore and select appropriate LLMs. Employ prompt engineering to improve the quality of outputs. Regularly evaluate and refine the prompts for optimal performance using automated evaluations in Prompt Flow.

  • Implement Effective Model Management and Deployment Strategies: When building custom models, practice robust model versioning, safe deployment strategies like blue-green deployment, and continuous monitoring. This ensures that the AI models remain effective, relevant, and safe over time, adapting to new data and user needs.

By following these recommendations, teams can build resilient, scalable, and effective AI solutions on Azure, ensuring that their deployments are not just successful but also sustainable in the rapidly evolving field of AI technology.