Imagine stepping into a world where artificial intelligence (AI) and machine learning (ML) aren't just about complex algorithms and code but about making things run smoother, safer, and on a bigger scale. Welcome to the world of PromptFlow, a tool that's not merely cutting edge—it's redefining the edge itself within the Azure Machine Learning & AI studio.
What is LLMOps?
LLMOps stands for Large Language Model Operations, a crucial framework that ensures the smooth operation of large language models from their development phase to their deployment and ongoing management. It involves crafting effective prompts for accurate responses, deploying these complex models seamlessly into production, and continuously monitoring their performance to guarantee they remain effective, safe, and within ethical boundaries. By integrating LLMOps principles, organizations can harness the full potential of their large language models, ensuring they're not only powerful but also responsibly managed.
LLMOps VS MLOps
MLOps is all about managing the entire life cycle of machine learning models, from cradle to grave. We're talking data preparation, model training, deployment, monitoring, and even retraining when necessary. It's a comprehensive approach to ensuring your ML models are performing at their best, no matter what stage they're in.
LLMOps, on the other hand, is a bit more specialized. It's focused specifically on the life cycle of large language models (LLMs). These bad boys are trained on massive amounts of text data and can be used for all sorts of cool stuff, like generating text, translating languages, and even answering questions in a way that's actually informative (imagine that!).
So, while MLOps is more of a catch-all for ML model management, LLMOps is tailored specifically for the needs of those massive language models. But hey, whether you're dealing with MLOps or LLMOps, the goal is the same: ensuring your models are running smoothly and delivering top-notch performance.
Implementing LLMOps with PromptFlow & Github Actions
With Azure ML CLI & YAML configurations for Github actions, automating PromptFlow workflows becomes a breeze. Think of it as setting up a series of dominoes; once you tip the first one, everything else follows smoothly. Here’s how it goes:
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Kick things off by checking out the repository.
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Get Azure ML CLI extension onboard—this is like adding a turbocharger to your Azure CLI.
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Log into Azure with Service Principal, using secrets as your backstage pass.
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Set up Python—choose your version, like picking the right gear for a road trip.
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Install PromptFlowdependencies—think of it as packing your bag with everything you need for the journey.
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Run PromptFlow—start your engines and begin the adventure, logging everything along the way.
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Set the run name—it's like naming your vessel for the voyage.
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Show off the current run name—a little bragging about your ship's name.
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Display PromptFlow results—time to look at the snapshots from your trip.
But why stop there? It's time to move on to evaluating results and registering models. It's somewhat like reaching your destination, checking if everything's as you expected, and then making your mark.
Taking it Further - a second action could then assert the evaluation results and do the model registration
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Start with checking out the repository again—returning to base.
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Install that Azure ML CLI extension once more—because why fix what isn't broken?
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Log back into Azure—hello again, old friend.
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Set up Python—still got to have the right gear.
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Configure Azure CLI with your subscription—like choosing the right map for the journey ahead.
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Install those dependencies again—can't embark without your essentials.
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Assert evaluation results— Execute a Python script to assert if the evaluation results meet a predefined quality threshold, set an output variable based on the assertion result.
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Register the PromptFlow model— If the assertion in the previous step is true (indicating the model meets the quality criteria), register the model using configurations from defined in your YAML file.
Follow up with a final action that automates the process of deploying & testing a PF workflow to Azure Machine Learning as an online-managed Endpoint.
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Check out the repository.
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Install Azure ML CLI extension.
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Azure login.
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Set up Python.
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Set default subscription.
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Create a unique hash: Generate a short hash to ensure the endpoint name is unique.
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Create and display a unique endpoint name: Modify the endpoint name to include the generated hash for uniqueness and displays it.
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Setup the online endpoint: Create an online endpoint using the YAML configuration.
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Update deployment configuration: Dynamically update the deployment YAML with specific Azure resource identifiers.
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Setup the deployment: Create an online deployment on the previously created endpoint, directing all traffic to it.
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Retrieve and store the principal ID: Obtain the principal ID of the managed identity associated with the endpoint and stores it for later use.
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Assign RBAC Role to Managed Identity: Assign the "Azure Machine Learning Workspace Connection Secrets Reader" role to the managed identity to grant necessary permissions.
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Wait for role assignment to propagate: Pause the workflow for 5 or 10 minutes to ensure the role assignment has taken effect.
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Check the status of the endpoint: Verify the operational status of the online endpoint.
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Check the status of the deployment: Retrieve and display logs for the deployment to ensure it is operating as expected.
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Invoke the model: Test the endpoint by invoking it with a sample request.
With these 3 action examples you can achieve a full LLMOps solution, but you must also consider the best practices as well as scaling up to meet future needs.
LLMOps Best Practices:
1. Crafting Your Team’s Blueprint - Think of this as assembling your superhero squad, where everyone from data scientists to security gurus knows their mission. And don’t forget to build a treasure chest (a.k.a. a documentation repository) where all your maps and tools are safely kept.
2. Centralize Data Management - Data is your goldmine, so manage it like a pro. Use a system that can handle the load without breaking a sweat, set rules to keep the gold polished (quality and privacy, anyone?), and keep track of your treasure’s versions for easy comebacks.
3. Automated Deployment and Continuous Monitoring- Make deploying your models as easy as waving a wand by using automation. Keep an eye on your creations with smart monitoring and let CI/CD pipelines be your fast track to improvement.
4. Fortifying Your Castle - Implement Security Measures - Security is key. Only let the trusted knights into your castle, put up magical shields around your data, and follow the kingdom’s laws (hello GDPR and CCPA) to protect the realm.
5. The Quest for Understanding -Promote Explainability - Use the powers of Explainable AI to peel back the curtain on your model’s decisions, hunt down biases like dragons, and foster a kingdom where transparency reigns supreme.
6. Embracing the Journey of Knowledge - Stay curious, collect insights like precious gems, and always be ready to adapt. Your models are living entities, growing and changing with the landscape.
7. The Ethical Compass - Adhere to Data Ethics - Navigate the high seas with honor. Follow the stars of ethical guidelines, ponder the impact of your actions, and keep a logbook to chart your course and hold yourself accountable.
PromptFlow is fantastic at deploying models for real-time scoring, but as your needs grow, so should your tools. Large-scale LLM applications might require additional mechanisms for scaling and load balancing across multiple instances of the Azure OpenAI Service.
Enter Azure API Management for scaling and balancing, ensuring your application runs smoothly, no matter the load.
Let’s explore following example illustrated by Andre Dewes in his Smart load balancing for OpenAI endpoints and Azure API Management blog post.
This sample leverages Azure API Management to implement a static, round-robin load balancing technique across multiple instances of the Azure OpenAI Service. The key benefits of this approach include:
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Support for multiple Azure OpenAI Service deployments behind a single Azure API Management endpoint.
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Abstraction of Azure OpenAI Service instance and API key management from application code, using Azure API Management policies and Azure Key Vault.
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Built-in retry logic for failed requests between Azure OpenAI Service instances.
By integrating Azure API Management, PromptFlow users can achieve scalability and high availability for their LLM applications, ensuring seamless operation even under heavy load.
In a nutshell, PromptFlow is not just a tool; it's a revolution in LLMOps, making the management of large language models not just possible but efficient, secure, and scalable. Its seamless integration with Azure DevOps and GitHub, coupled with its adaptability across different project stages, makes it a premier solution for anyone looking to enhance their AI operations
Are you struggling with taking your LLM application into production? Reach out to discover the expansive potential of LLMs for your enterprise. To learn more about implementing LLMs in your enterprise, download our LLM Workshop flyer or get in touch with us today.
