Now that you are familiar with moving from a POC to MVP, the next key transition is moving from MVP to production rollout. This is where the focus must be put on the requirements and setup involved in a production deployment with considerations for the requirements of the end user.
Before a single line of code is deployed, start a collaboration across technical and business stakeholders. Ask these critical questions:
- MVP outcome: Did the user feedback regarding the MVP and its results meet the desired expectations? Did the rollout of the MVP successfully fulfill and support the business objectives and achieve the intended outcome?
- LLM Model outcomes: Can the selected LLM models meet and accomplish the goals
- End Users: Is this solution for internal teams or external customers? Security, access controls, and user experience needs will differ significantly.
- Data Segregation: Are there multi-tenant concerns, or is a need for strict boundaries around data access for different teams? Azure provides tools to enforce this, including Azure Active Directory (Azure identity & access security best practices | Microsoft Learn) and RBAC (What is Azure role-based access control (Azure RBAC)? | Microsoft Learn).
- Security: How sensitive is the data? Outline your encryption, authentication, and compliance strategy early on.
- Scalability: Estimate requests per minute (RPM) and transactions per month (TPM). Design for surges of traffic based on historic data or expected upcoming peaks.
- Token Requirements: Does your model need to handle larger volumes of text or code than standard OpenAI allowances? Does solution require caching support for enhanced and efficient outcome?
- Cost Allocation: Will internal teams need to be cross-charged? Can solution track the token usage to manage the cost and apply any quota within business units?
Before deploying your Azure OpenAI solution into production, carefully consider your target audience, as this will dictate security protocols, access controls, and user experience design. Prioritize data security by planning encryption and authentication, especially for sensitive information. If multiple teams or customers will use the system, create secure boundaries to protect each entity's data. For smooth operation and cost management, estimate potential traffic and ensure the chosen model can handle your expected workload.
After evaluating above criteria, the next step is to reduce risk and increase success during the production rollout. A good rollout is like a solid base for your Azure OpenAI solution. Let's look at three main elements: the gradual approach, deployment checklists, and preparing contingency plans.
Before there is any production rollout, let’s consult with a deployment checklist. This will heavily depend on your individual business needs, but many are likely to cross over across all use-cases.
- Infrastructure Readiness: Ensure that Azure resources (compute, storage, networking, availability region) are provisioned and configured correctly. See the Azure OpenAI landing zone reference architecture: Azure OpenAI Landing Zone reference architecture (microsoft.com)
- Model Deployment: Automate the process of deploying your OpenAI model, including its configuration and any pre/post-processing steps (https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython-new).
- Integration Verification: Thoroughly test how your solution interacts with existing systems and data sources. How will the frontend app need to connect to the OpenAI model?
- Security Checkpoints: Double-check user authentication, data encryption, and any compliance requirements.
- Monitoring Setup: Make sure that you have logging and alerting systems ready as you approach go-live. What metrics will you use to measure the model's performance? Do you plan to do continuous training on the model to enhance it over time?
The reference design above from MVP to Production and has basic foundational components and essential elements for a live deployment.
Once the readiness has been confirmed, then begins the rollout. There are many ways to conduct a rollout, one of the safest and most recommended is a phased approach. A phased approach involves breaking down your Azure OpenAI deployment into smaller, manageable stages. Instead of launching the entire solution at once, you roll it out incrementally, starting with a pilot group or a limited set of features. This allows you to gather real-world feedback and identify potential issues, and refine your solution before expanding to a wider audience. With a phased approach, you minimize disruption, control risk, and ensure a smoother, more successful transition into production.
Characteristics and benefits of a phased approach:
- Real-World Testing: Deploying to a smaller pilot group allows you to closely observe how your solution handles real-world data and user interactions in a controlled environment.
- Iterative Improvement: The valuable feedback you collect from your pilot users enables you to polish the model, modify interfaces, and change security settings before expanding to a larger audience. This is where LLMOps assists you.
- Gradual Scalability: A phased approach lets you monitor infrastructure performance under growing load and adjust resources (redundant, multi region) as needed, preventing costly overprovisioning or unexpected downtime.
- Minimized Disruption: Issues discovered during a test deployment with a limited group are far less disruptive than those surfacing after a full-scale launch.
How might a phased rollout look in practise? It might look like this....
- Internal Pilot: Start with a select group of users within your organization, providing clear guidance on how to provide feedback.
- Iterative Improvement: Use that pilot feedback to refine the model, address UI issues, and solidify integration with your document management system.
- Expansion: Gradually increase the pilot group size, monitoring performance and scalability.
- Full Rollout: Confident in your solution, release it to the entire organization with comprehensive training materials.
Remember: A phased approach gives you the agility to learn, adapt, and ensure a successful, well-received Azure OpenAI deployment.
Monitoring is essential for a smooth and successful Azure OpenAI deployment. Real-time visibility into your solution's performance enables proactive problem-solving, allowing you to address issues before they become major disruptions. Monitoring data also guides optimization efforts, revealing opportunities to refine your model, scale resources appropriately, or improve the user experience based on observed patterns. Reliable monitoring and well-defined alerts foster user trust, demonstrating your commitment to a robust and well-maintained solution. Azure provides robust monitoring tools to ensure your OpenAI solution runs smoothly. Utilize Azure Monitor to track key performance metrics, logs, and set up alerts for potential issues. For deeper application-level insights, leverage Application Insights to track performance, errors, and how your users interact with the solution. For detailed guidance, refer to Microsoft's Azure OpenAI monitoring documentation: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/monitoring
Some other considerations for deployment include:
- Business continuity: If your application critical, be sure to ensure business continuity through cross region deployment: Enable disaster recovery across Azure regions across the globe - Azure Site Recovery | Microsoft Lea...
- Consider including our GenAI Gateway capabilities in APIM : Introducing GenAI Gateway Capabilities in Azure API Management - Microsoft Community Hub
- Scaling using PTU & PAYG: Azure OpenAI Service Provisioned Throughput Units (PTU) onboarding - Azure AI services | Microsoft L...
- Responsible AI: In order to mitigate risks, please follow Microsoft’s Responsible AI guidance: Responsible and trusted AI - Cloud Adoption Framework | Microsoft Learn
While it isn't without its challenges, careful preparation, strategic rollouts, and continuous improvement are the keys to unlocking the full potential in the deployment. By approaching your deployment thoughtfully, you won't simply implement a powerful piece of technology; you'll create a scalable, secure, and user-centric solution that delivers tangible value to your organization or customers. Remember, your deployment journey is about more than the technology itself – it's about harnessing AI to drive innovation.
References:
@Paolo Colecchia @Taonga_Banda @renbafa @arung Morgan Gladwell