This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.
Our host, David Blank-Edelman and our armchair architects Uli Homann and Eric Charran will be discussing how to transition from proof-of-concept (POC) to production (Prod) for AI solutions. It’s one thing to have a POC but what factors should solution architects be mindful of when taking that POC and moving into a real-world production environment?
Below are some key considerations to keep in mind:
- Scale with Confidence First and foremost, scale is critical. Ensuring that your chosen model or hosted foundational model can handle queries and API requests efficiently is paramount. It's not just about handling the volume but also about delivering valuable responses promptly.
- Seamless Integration Next, integration with existing systems cannot be overlooked. The API service of your model partner should be robust, secure, and offer features like schematization of prompts and returns, confidence intervals for rating the LLM's responses, and comprehensive error handling.
- Cost Engineering and Security Cost engineering is another vital aspect. With services like OpenAI's GPT versions offering multiple models, choosing the right one can significantly impact both performance and budget. Additionally, ensuring that your hosting system aligns with your enterprise security model is essential for safeguarding access to endpoints.
- Observability and Content Safety Lastly, observability and content safety are key. Establishing guardrails to process prompts securely and monitoring the right metrics from a customer's perspective ensures a secure and reliable system.
Keep in mind that each step is a building block towards a successful and sustainable production environment. Stay tuned for more insights from the Armchair Architects on the Azure Enablement Show.
Watch the episode below or watch more episodes in the Armchair Architects Series.
Resources
- Training: Scale AI in your organization
- Architectural approaches for AI and ML in multitenant solutions
- Azure AI Content Safety documentation
- Plan to manage costs for Azure OpenAI Service
- What is Azure AI Content Safety?
- Watch more episodes in the Armchair Architects Series
Related Blogs
- Armchair Architects: How to choose a LLM partner for your AI project
- Armchair Architects: What Is Responsible AI?
- Armchair Architects: Are AI Charters Valuable?
- Armchair Architects: Considerations for Ethical and Responsible Use of AI in Applications
- Armchair Architects: Large Language Models (LLMs) & Vector Databases
- Armchair Architects: Artificial Intelligence, Large Language Models, and Architects (Part 1 of 2)
- Armchair Architects: Artificial Intelligence, Large Language Models, and Architects (Part 2 of 2)
