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Update pyproject.toml and Docs (#7274)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yaofu <voipman@sina.cn>
Co-authored-by: Umit Kacar, PhD <kacarumit.phd@gmail.com>
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@ -30,13 +30,15 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale.
- [TensorBoard](https://tensorboard.dev/): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
## Deployment Integrations
- [Neural Magic](https://neuralmagic.com/): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [OpenVino](openvino.md): OpenVINO is Intel's toolkit for optimizing and deploying computer vision models efficiently across various Intel hardware platforms.