New Video: Build Self-Improving AI Agents with the NVIDIA Data Flywheel Blueprint

NVIDIA Data Flywheel Blueprint Overview

The NVIDIA Data Flywheel Blueprint aims to optimize AI agents by automating experimentation to reduce inference costs, improve latency, and enhance effectiveness. It utilizes NeMo and NIM microservices to distill, fine-tune, and evaluate smaller models using real production data.

Steps to Implement the Data Flywheel Blueprint

1. Initial setup: Use NVIDIA Launchable to deploy required GPU compute, set up NeMo and NIM microservices, and clone the Data Flywheel Blueprint GitHub repo.

2. Ingest and curate logs: Collect production agent interactions, store logs in Elasticsearch, and utilize the flywheel orchestrator to tag, deduplicate, curate datasets, and run experiments.

3. Experiment with models: Evaluate existing and newer models, fine-tune smaller models using different setups, measure accuracy and performance, and select models that match or outperform the baseline.

4. Deploy and improve continuously: Deploy efficient models in production, retrain with new data, and repeat the flywheel cycle for continual improvement.

How to Get Started

Watch the how-to video or download the Data Flywheel Blueprint from the NVIDIA API Catalog to begin optimizing AI agents efficiently.