Building Effective AI Agents: Leveraging Data Flywheels at NVIDIA

Welcome to my LinkedIn video, where I'll explore what it takes to build effective AI agents that stay relevant and helpful over time, and introduce the concept of data flywheels as a key component in achieving this goal.

1. Silendrin works with Nvidia on the generative AI platforms team.
2. This video is about building effective AI agents that stay relevant and helpful over time.
3. The focus is not on using the biggest language models, but on simple data flywheels.
4. Data flywheels involve continuous loops of data processing, curation, model customization, evaluation, and guardrailing for safer interactions.
5. AI agents should be able to capture and learn from user feedback to refine their responses.
6. Building and scaling AI agents can be painful, especially when dealing with rapidly changing data and increasing costs.
7. Data flywheels help by continuously curating data, ground truth, user feedback, and business intelligence to experiment with and evaluate existing and newer models.
8. The goal is to surface efficient, smaller models that provide accuracy parity with larger language models but offer lower latency, faster inference, and lower total cost of ownership.
9. Nvidia recently announced Nemo microservices, an end-to-end platform for building powerful agentic and generative AI systems and data flywheels.
10. Nemo microservices include components for each stage of the data flywheel loop: curation, customization, evaluation, guardrails, and retrieval.
11. These microservices are exposed as simple-to-use API endpoints, allowing users to customize large language models, evaluate them, guardrail them, and build state-of-the-art retrieval pipelines.
12. Users have the flexibility to run Nemo microservices anywhere: on-premises, in the cloud, in data centers, or even on the edge.
13. With NV support, users get enterprise-grade stability and support.
14. A sample data flywheel architecture using Nemo microservices involves an end user interacting with a frontend agent, which is guardrailed for safer interactions.
15. The backend serves an optimized model for inference, and a data flywheel loop constantly curates the data store, retrains models, and evaluates them to promote the most accurate model for use.
16. Nvidia adopted and built a data flywheel for their NV info agent, an internal employee support chatbot agent that helps answer queries across various domains.
17. The underlying data wheel architecture involves guardrailing user interactions for safety, using a router agent orchestrated by multiple expert agents.
18. Each expert agent handles specific domains and is augmented with retrieval pipelines to fetch relevant information.
19. A data flywheel loop constantly builds on user feedback and production data inference logs to evaluate and promote the most effective models as guardrailed NYM models for the router agent.
20. The video discusses a real-world case study of how Nvidia adopted and built this data flywheel for their NV info agent.
21. Data flywheels help ensure accurate routing to expert agents using faster and more cost-effective language models by continuously curating ground truth data points from user feedback and productio
22. By fine-tuning smaller models with a limited dataset, you can achieve accuracy comparable to larger models while significantly reducing latency and inference costs.
23. The key to building effective data flywheels is monitoring user feedback, analyzing model drift or inaccuracies, classifying errors, attributing failures, creating ground truth datasets, planning
24. To get started with building your own data flywheels, explore Nemo microservices and NVIDIA NIM for agentic use cases, using a framework that includes monitoring user feedback, planning, executing

Source: AI Engineer via YouTube

❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!