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8 changes: 6 additions & 2 deletions nemo/data-flywheel/embedding-finetuning/README.md
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Expand Up @@ -24,7 +24,11 @@ The tutorial covers the following steps:

### About NVIDIA NeMo Microservices

The NVIDIA NeMo microservices platform provides a flexible foundation for building AI workflows such as fine-tuning, evaluation, running inference, or applying guardrails to AI models on your Kubernetes cluster on-premises or in cloud. Refer to [documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for further information.
NVIDIA NeMo is a modular, enterprise-ready software suite for managing the AI agent lifecycle, enabling enterprises to build, deploy, and optimize agentic systems.

NVIDIA NeMo microservices, part of the [NVIDIA NeMo software suite](https://www.nvidia.com/en-us/ai-data-science/products/nemo/), are an API-first modular set of tools that you can use to customize, evaluate, and secure large language models (LLMs) and embedding models while optimizing AI applications across on-premises or cloud-based Kubernetes clusters.

Refer to the [NVIDIA NeMo microservices documentation](https://docs.nvidia.com/nemo/microservices/latest/about/index.html) for further information.

### About the SPECTER dataset

Expand Down Expand Up @@ -90,4 +94,4 @@ Ensure you have access to:
uv run jupyter lab --ip 0.0.0.0 --port=8888 --allow-root
```

5. Navigate to the [data preparation notebook](./1_data_preparation.ipynb) to get started.
5. Navigate to the [data preparation notebook](./1_data_preparation.ipynb) to get started.