Need for an AI Data Flywheel
AI agents, unlike traditional systems, operate autonomously, reason through complex scenarios, and make decisions in dynamic environments. As these systems evolve and enterprises begin to build multi-agent systems, where AI agents integrate across platforms and collaborate with human teams to enhance operations, it becomes increasingly challenging to keep the entire system up-to-date to remain relevant and effective.
The solution lies in adopting a data flywheel strategy, where each model powering every agent is continuously adapted by learning from feedback on its interactions. A data flywheel is a self-reinforcing loop in which data from human feedback, real-world, and AI interactions continuously enhances the system, enabling it to adapt and refine decision-making (Figure 1).
Figure 1. Data flywheel example architecture
Develop and Deploy AI Agents with NVIDIA NeMo Microservices
NVIDIA NeMo microservices are an end-to-end, fully-accelerated platform for building data flywheels. You can simplify the development and deployment of agentic systems using industry-standard APIs and Helm charts. You can also set up data flywheels that continuously update your AI agents with the latest information, all while retaining full control over proprietary data.
Simplify AI Data Flywheels with NeMo Microservices
NeMo microservices offers the following set of powerful tools to manage the entire lifecycle of an AI agent and build efficient data flywheels to constantly improve the underlying models with fresh, relevant data to enable continuous improvement, adaptability, and compounding value in AI-driven systems:
- NeMo Curator: GPU-accelerated modules for curating high-quality, multi-modal training data.
- NeMo Customizer: High-performance, scalable microservice that simplifies the fine-tuning of large language models (LLMs) for downstream tasks.
- NeMo Evaluator: Automated evaluation of custom AI models using academic and custom benchmarks.
- NeMo Retriever: Fine-tuned microservices to build AI query engines with scalable document extraction and advanced retrieval-augmented generation (RAG) for multimodal datasets.
- NeMo Guardrails: Seamless orchestrator for building robust safety layers to ensure accurate, appropriate, and secure agentic interactions.
- NIM Operator: Kubernetes Operator that is designed to facilitate the deployment, management, and scaling of NeMo and NIM microservices on Kubernetes clusters.
NeMo Guardrails
With AI agents driving critical business operations including decision making and customer interactions, ensuring that AI models remain safe and aligned with organizational policies is essential.
NeMo Guardrails lets you easily define, orchestrate and enforce AI guardrails in agentic AI applications, detecting up to 99% of policy violations with only a sub-second latency trade-off. It enforces various safety measures such as content moderation, off-topic dialogue moderation, hallucination reduction, jailbreak detection, and protection of personally identifiable information (PII).
NeMo Guardrails enables the addition of programmable safety layers throughout the AI interaction process, making it easy to integrate these controls into applications, including input, dialog, retrieval, execution, and output rails to ensure alignment with safety expectations and policies.
NeMo Guardrails scales seamlessly to support multiple applications with diverse guardrail configurations. It integrates with third-party and community safety models as well as the NemoGuard JailbreakDetect, Llama 3.1 NemoGuard 8B ContentSafety, and Llama 3.1 NemoGuard 8B TopicControl NVIDIA models for highly specialized, robust protections..
Figure 8. NVIDIA NeMo Guardrails usage architecture and capabilities
NIM Operator
NeMo and NIM microservices can be deployed individually using containerized Kubernetes distributions and Helm charts. However, when multiple NIM and NeMo microservices are combined to create sophisticated agentic systems, such as the NVInfo Bot, managing the end-to-end lifecycle of these microservices can present major challenges for cluster administrators and developers.
The NVIDIA NIM Operator streamlines AI inference workflow orchestration with Kubernetes-native Operators and Custom Resource Definitions (CRDs), enabling automated deployment, lifecycle management, intelligent model pre-caching for reduced latency, and simplified auto-scaling. By eliminating infrastructure complexities, it enables you to focus on innovation.
Get Started with NeMo Microservices
As AI continues to transform industries, the importance of keeping AI agents updated and effective will only increase. NVIDIA NeMo
