Building Effective AI Agents: Leveraging Data Flywheels for Scalable Performance at NVIDIA

Join Silendrin, a generative AI platforms expert at Nvidia, as he explores what it takes to build effective AI agents that stay relevant and helpful over time - and how simple data flywheels can make all the difference.

1. Silendrin works on generative AI platforms at Nvidia.
2. The video discusses building effective and relevant AI agents over time, focusing on "data flywheels."
3. AI agents can take various forms, including customer service, security, and research agents.
4. AI agents should perceive, reason, act, and learn from user feedback to improve accuracy and usefulness.
5. Building and scaling AI agents can be challenging due to rapidly changing data and increasing costs.
6. Data flywheels help by creating a continuous loop of data processing, curation, model customization, evaluation, and safer interactions.
7. Data flywheels provide relevant, accurate responses in production environments and surface efficient models with lower latency and cost.
8. Nvidia recently announced Nemo microservices, an end-to-end platform for building powerful agentic and generative AI systems and data flywheels.
9. Nemo microservices include components for each stage of the data wheel loop, such as curation, customization, evaluation, guardrails, and retrieval.
10. These microservices are easy to use with simple API endpoints and can run on-premises, in the cloud, or at the edge.
11. A sample data flywheel architecture using Nemo microservices involves an end user interacting with a front-end agent, which is guardrailed for safer interactions.
12. The back-end serves an optimized model for inference and uses a data flywheel loop to curate data, retrain models, and evaluate them.
13. Once a certain model meets the target accuracy, it can be promoted as the underlying model for the agentic use case.
14. Nvidia adopted and built a data flywheel for its NVinfo agent, an internal employee support agent that helps Nvidia employees with access to enterprise knowledge across multiple domains.
15. The NVinfo agent is a customer service or employee support chatbot agent that can help answer queries across various domains.
16. The underlying data wheel architecture for this agent involves guardrailing user interactions, using an LLM-orchestrated router agent with multiple expert agents underneath, and setting up a data
17. Using subject matter experts and human-in-the-loop feedback, the ground truth is continuously curated, and Nemo customizer and evaluator are used to evaluate multiple models and promote the most e
18. The router agent depends on the user query to understand the intent and context and guides or routes the query to one of the expert agents.
19. A smaller variant LLM can be used for the router agent, providing faster and more cost-effective inference while still accurately routing queries to the correct expert agent.
20. Data flywheels help organizations compare and contrast various models, curate data points, and fine-tune smaller models for specific use cases.
21. By deploying smaller models, businesses can achieve significant savings in terms of lower inference cost, model size reduction, and lower latency.
22. Building effective data flywheels involves monitoring user feedback, analyzing errors, attributing failures, creating ground truth datasets, planning and executing model selection, fine-tuning, re
23. Nvidia provides tools and frameworks to help build agentic use cases and data flywheels.
24. The presentation aims to inspire viewers to think about building not just agentic use cases but also data flywheels around them using NVDIA tools and frameworks.

Source: AI Engineer via YouTube

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