Data Flywheel
A data flywheel is a feedback loop where data collected from interactions or processes is used to continuously refine AI models, which in turn generates better outcomes and more valuable data.
How Do AI Data Flywheels Work?
AI data flywheels work by creating a loop where AI models continuously improve by integrating institutional knowledge and user feedback. As the model generates outputs, it collects feedback and new data, which are then used to refine and enhance the model. This process involves curating and improving the quality of the data, ensuring that the AI model’s accuracy and performance are consistently enhanced.
Figure 1: Data Flywheel: A continuous cycle of data processing, model customization, evaluation, guardrails, and deployment that uses enterprise data to improve AI systems
Additionally, AI guardrails are in place to maintain the integrity and reliability of the data, ensuring that the outputs are accurate, compliant, and secure. This continuous cycle of feedback and enhancement makes the AI model increasingly effective over time.
This workflow process involves six key steps:
1. Data Processing: An AI data flywheel starts with enterprise data, which takes many forms—including text documents, images, videos, tables, and graphs. For an AI data flywheel, data processing is required to extract and refine raw data. The raw data is further filtered to remove low-quality documents, personally identifiable information (PII), and toxic or harmful data to generate high-quality data. This curation leads to higher accuracy for the application.
2. Model Customization: Using large language model (LLM) techniques like domain adaptive pretraining (DAPT) and supervised fine-tuning (SFT), you can add domain-specific knowledge and task-specific skills to the model quickly with lower resource requirements. The model now has a deeper understanding of the company’s unique vocabulary and context.
3. Model Evaluation: Then, you can evaluate the model’s performance to verify the answers (outputs) align to application requirements. These first three steps are done iteratively to ensure that the model’s quality is improved and the results are satisfactory for the intended application.
4. AI Guardrails Implementation: Adding AI guardrails to your customized model ensures that enterprises’ specific privacy, security, and safety requirements are met when deploying the application.
5. Custom Model Deployment: When deploying both generative AI and agentic AI applications, constant retrieval of information from a growing set of databases takes place. User feedback and system activity is collected repeatedly. With an AI data flywheel, you can generate refined, smarter answers while building institutional knowledge based on how the application is being interacted with.
6. Enterprise Data Refinement: As a result, your institutional data is continuously updated over time with new data collected from human and AI model feedback. This feeds back into data processing as the process is repeated.
What Is the Purpose of a Data Flywheel Strategy for Scaling AI?
Real-world AI agent systems may have hundreds to thousands of AI agents simultaneously working collectively to automate processes. A data flywheel is imperative to streamline agent operations (e.g., reviewing new data), especially as business requirements change. This guarantees smoother AI agent orchestration, as a specialized team of AI agents can provide resource-optimized plans and execute on those plans with minimal human input.
Agentic AI scalability depends on an automated cycle of data curation, model training, deployment, and institutional knowledge collection and review to improve the intelligent agents’ performance.
In addition, AI applications involve a number of human collaborators with specific responsibilities:
| Role | Responsibility |
|---|---|
| Data engineers | Must curate structured and unstructured data to generate high-quality data for training AI models |
| AI software developers | Must take the curated datasets to train the AI model further for specialized purposes |
| IT and MLOps teams | Must deploy the model in a safe environment while considering usage and access requirements |
| Human-in-the-loop and AI systems | Must review the institutional knowledge generated and make consistent adjustments to the database, as it is continuously fed back into the data engine |
Why Are Data Flywheels Important for Adopting Agentic and Generative AI?
When adopting AI agent and generative AI applications, a data flywheel is needed to drive the continuous improvement and adaptability of your software. For example, as business requirements change or grow in complexity, performance and cost often become a differentiating factor for success.
With an effective AI data flywheel, organizations can:
- Develop cost-effective applications tailored to business or customer needs.
- Personalize and refine offerings to improve user experiences.
- Meet tangible goals, such as increasing sales conversions or implementing automations to improve productivity.
To maintain a competitive edge, organizations can gather and process new interaction data, refine their AI models, and progressively enhance their AI applications’ performance. From LLMs to vision language models (VLMs), a variety of data can be integrated.
Development teams can also speed up model training and instead focus on fine-tuning existing foundation models with their proprietary data. Generative AI microservices simplify this process further with an API call.
This approach can significantly reduce the time and resources required to develop and deploy agentic and generative AI solutions.
When Should You Accelerate Your Data Flywheel?
Accelerating your data flywheel for AI is necessary to address dependencies associated with agentic AI technology.
For example, without a centralized system for feedback and logging, it’s difficult to track and analyze system performance, which can slow down the data flywheel. Evaluation datasets that don’t accurately reflect real-world scenarios can lead to models that perform poorly.
As knowledge bases are updated, the relevance of system feedback can decline, making it harder for the flywheel to continuously improve. Human intervention, while beneficial, is resource-intensive and time-consuming. Addressing this is crucial for accelerating the data flywheel and maintaining its effectiveness.
As such, acceleration becomes necessary when many interactions are happening on a system level that impact performance. For example, in generative AI applications, accuracy and alignment to human preferences are important. In agentic AI applications, streamlined plans and execution of those plans by AI knowledge workers are required.
| Operational Requirements | Recommendation |
|---|---|
| Facilitating resource-intensive tasks, such as training data review | With centralized user data collection and automatic insight generation, user data classification and triaging streamlines human-in-the-loop review. |
| Enhancing agentic AI and generative AI applications by refining models | A data flywheel can be powered with a Helm chart deployment or via API calls for specific parts of your workflow. |
| Running secure deployments and protecting enterprise data | Running end-to-end workflows on a GPU-accelerated cloud or private data center provides higher security, privacy, control, and integration flexibility. |
How Can You Get Started With Data Flywheels?
Building the next generation of agentic AI and generative AI applications using a data flywheel involves rapid iteration and use of institutional data.
NVIDIA NeMo™ is an end-to-end platform for building data flywheels, enabling enterprises to continuously optimize their AI agents with the latest information.
NeMo helps enterprise AI developers easily curate data at scale, customize LLMs with popular fine-tuning techniques, consistently evaluate models on industry and custom benchmarks, and guardrail them for appropriate and grounded outputs.
The NeMo platform includes:
- NeMo Curator: To efficiently curate high-quality datasets for training LLMs, thereby enhancing model performance and accelerating the deployment of AI solutions.
- NeMo Customizer: A high-performance, scalable microservice that simplifies the fine-tuning and alignment of LLMs with popular parameter-efficient fine-tuning techniques including LoRA and DPO.
- NeMo Evaluator: An enterprise-grade microservice that provides industry-standard benchmarking of generative AI models, synthetic data generation, and end-to-end RAG pipelines.
- NeMo Guardrails: A microservice for developers to implement robust safety and security measures in LLM-based applications, ensuring that these applications remain reliable and aligned with organizational policies and guidelines.
- NeMo Retriever: A collection of microservices that drive the AI data flywheel by enabling scalable data ingestion and high-accuracy, privacy-preserving retrieval. With fast, context-aware responses from large data collections, developers are able to connect AI applications to diverse data sources, build AI query engines, and continuously refine AI models with real-time insights.