NVIDIA data flywheel: The new operational model for continuous GenAI improvement

Many enterprise Generative AI initiatives begin with strong momentum but lose effectiveness over time. As usage increases, model performance often declines, user trust declines, and operational costs rise. The key issue stems from the underlying development approach. 

Enterprises now require systems that can learn and adapt based on real-world usage. GenAI outputs are evaluated instantly by users, creating feedback that can be used to improve future performance. Organizations that can incorporate this feedback quickly will maintain higher relevance, lower cost, and stronger adoption. 

The NVIDIA Data Flywheel offers a structured approach to address this challenge. It provides a closed-loop system that converts production usage into continuous model improvements.

Why CRISP-DM falls short in the GenAI era

To understand why enterprises need a new framework like the data flywheel, it is important to understand the limitations of the CRISP-DM model. 

CRISP-DM (cross industry standard process for data mining) has served analytics and machine learning projects well for years. Its lifecycle includes business understanding, data preparation, modeling, evaluation, and deployment. However, it assumes a static and linear process. 

Generative AI changes the operating model in three critical ways:

• Users interact with the system and evaluate outputs in real time.

• Feedback from actual usage becomes a source of training data.

• Business value increases with the speed at which feedback is incorporated. 

While CRISP-DM outlines a foundational process, it does not support the real-time, iterative nature of GenAI. The data flywheel is designed to operate in this new environment. It enables systems to improve continuously through structured feedback loops.

Turning interactions into intelligence with the flywheel

NVIDIA data flywheel is a closed-loop system that improves model performance through real-world usage. The cycle functions as follows:

• Users submit prompts, evaluate outputs, and provide feedback.

• These interactions are captured and curated into structured training datasets.

• Models are fine-tuned or distilled using this data.

These steps form the core loop of the data flywheel, where every interaction becomes a source of learning and improvement.

Exhibit: Turning interaction data into intelligence using the data flywheel

The six-step blueprint for a production-ready flywheel

NVIDIA formalizes the flywheel into a repeatable production framework. This model includes six integrated stages:

1. Capture production data: Enterprises collect prompts, generated outputs, user corrections, retrieval logs, feedback scores, and other interaction signals.

2. Curate and synthesize training signals: The raw data is processed into high-quality training inputs. These inputs support supervised fine-tuning, reinforcement learning with human feedback (RLHF), or model distillation.

3. Fine-tune candidate models: Using NeMo services, enterprises can fine-tune large or domain-specific models. Distillation techniques may also be used to replicate the behavior of a larger model with a smaller, more cost-effective version.

4. Evaluate rigorously: Candidate models are tested on held-out datasets that reflect real production scenarios. Evaluation criteria include quality (such as accuracy and relevance), efficiency (such as latency and token cost), and safety (including adherence to guardrails). Only models that outperform the current production baseline are promoted. Additionally, guardrails ensure that the model meets the solution’s safety, security, and privacy requirements.

5. Deploy seamlessly: NVIDIA Inference Microservices (NIM) enables deployment through standardized, pre-optimized containers. Its APIs support quick model replacement, A/B testing, and rollback without requiring system-level changes.

6. Gather intrinsic & extrinsic feedback: The AI system captures rich information which is retrieved and generated with environmental interaction. This creates domain-specific corpus which when fed into the Flywheel as a feedback loop improves system performance and reliability over time.

Together, these six stages create a framework for continuous improvement. As each cycle runs, model quality improves, leading to increased usage and further optimization.

Driving enterprise value across the GenAI lifecycle

The data flywheel delivers measurable improvements across every stage of the model lifecycle:

• Data becomes training signals: Logs are transformed into structured and useful inputs.

• Signals enable model efficiency: Fine-tuning and distillation improve performance while reducing resource consumption.

• Improved models enhance user experience: Greater accuracy and relevance build trust and drive adoption.

• Deployment ensures compliance and control: Standard APIs and containers simplify audits, version control, and rollback procedures.

The result is a system that grows more valuable with each use, creating a long-term advantage for enterprises.

How Fractal uses the flywheel to unlock enterprise value

Fractal transforms NVIDIA’s data flywheel from a technical framework into a business-aligned engine for AI acceleration through its centralized feedback nexus. This approach connects system-level feedback with measurable outcomes, bridging the gap between AI performance and enterprise value.

The feedback nexus serves as the control center, linking every core layer of Fractal’s GenAI stack. Here's how it enables seamless coordination across the full AI lifecycle:

This approach enables enterprises to:

• Ensure model performance improves with usage and user feedback.

• Reduce manual intervention and training bottlenecks.

• Operate GenAI systems that align closely with business goals and real-time needs.

Conclusion

The CRISP-DM model helped enterprises develop machine learning capabilities. However, generative AI requires a new kind of operational framework. The NVIDIA data flywheel addresses this need by enabling systems to improve continuously by turning real-world interactions into training data and better models.

This approach is not just efficient. It is necessary for enterprises that want their GenAI systems to remain relevant, trustworthy, and cost-effective.