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Home»ADOPTION NEWS»NVIDIA NEMO Optimization of AI Agent with Micro Service and Data Fly Wheel
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NVIDIA NEMO Optimization of AI Agent with Micro Service and Data Fly Wheel

By Crypto FlexsApril 23, 20252 Mins Read
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NVIDIA NEMO Optimization of AI Agent with Micro Service and Data Fly Wheel
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Iris Coleman
April 23, 2025 07:27

NVIDIA NEMO Micro Service Use your data flywheel to improve your AI agent to ensure the efficiency and accuracy of your business environment.





As enterprise data evolves, it is becoming increasingly difficult to maintain AI system accuracy. NEMO micro service in NVIDIA provides solutions by integrating data flywheel that can continue to learn and adapt to new information, according to SHASHANK VEIDIA’s blog posts.

The importance of data flywheel

The data flywheel creates a self -reporting cycle where user interaction data improves the AI ​​model. This improved model offers better results, attracting more users and creating additional data. This continuous cycle is important for agent AI systems that must adapt to changing business needs.

Challenge

AI applications can be less accurate due to inputs and tools that evolve in the production environment in the production environment. For example, the banking AI agent, which querys the SQL database, may have difficulty if the organization does not re -educate and moves to the new MongoDB data set.

Efficiency

It is important to maintain efficiency as the AI ​​agent becomes more complicated. The increase in trading volume increases the cost of calculation in the case of agent AI systems, especially the agent AI system that requires multiple reasoning. Custom technology can optimize small models to reduce waiting time and total cost of ownership.

NVIDIA NEMO micro service

NVIDIA NEMO micro service provides a comprehensive platform for building a data flywheel so that companies can continue to optimize AI agents. These services are facilitating data cue to industry benchmarks, model customization and evaluation so that the AI ​​agent is maintained efficiently and accurately.

End -to -end pipeline construction

To configure the end -to -end pipeline using the NEMO micro service, developers can distribute the platform to the Kubernetes support system. This process includes data preparation, entity management, model fine adjustment, reasoning, evaluation and guard rails to maintain content safety.

By performing these steps, Enterprises can set up a data flywheel so that the AI ​​system can continue to adapt and improve. NVIDIA NEMO can download micro service, so it provides the tools needed to effectively implement this strategy.

For more information, you can access the details and tutorials provided on the NVIDIA website.

Image Source: Shutter Stock


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