Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/integrations/dvc.md

@@ -6,7 +6,7 @@ keywords: YOLOv8, DVCLive, experiment tracking, machine learning, model training
 
 # Advanced YOLOv8 Experiment Tracking with DVCLive
 
-Experiment tracking in machine learning is critical to model development and evaluation. It involves recording and analyzing various parameters, metrics, and outcomes from numerous training runs. This process is essential for understanding model performance and making data-driven decisions to refine and optimize models.
+Experiment tracking in [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) is critical to model development and evaluation. It involves recording and analyzing various parameters, metrics, and outcomes from numerous training runs. This process is essential for understanding model performance and making data-driven decisions to refine and optimize models.
 
 Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) transforms the way experiments are tracked and managed. This integration offers a seamless solution for automatically logging key experiment details, comparing results across different runs, and visualizing data for in-depth analysis. In this guide, we'll understand how DVCLive can be used to streamline the process.
 
@@ -16,7 +16,7 @@ Integrating DVCLive with [Ultralytics YOLOv8](https://www.ultralytics.com/) tran
   <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/dvclive-overview.avif" alt="DVCLive Overview">
 </p>
 
-[DVCLive](https://dvc.org/doc/dvclive), developed by DVC, is an innovative open-source tool for experiment tracking in machine learning. Integrating seamlessly with Git and DVC, it automates the logging of crucial experiment data like model parameters and training metrics. Designed for simplicity, DVCLive enables effortless comparison and analysis of multiple runs, enhancing the efficiency of machine learning projects with intuitive data visualization and analysis tools.
+[DVCLive](https://dvc.org/doc/dvclive), developed by DVC, is an innovative open-source tool for experiment tracking in machine learning. Integrating seamlessly with Git and DVC, it automates the logging of crucial experiment data like model parameters and training metrics. Designed for simplicity, DVCLive enables effortless comparison and analysis of multiple runs, enhancing the efficiency of machine learning projects with intuitive [data visualization](https://www.ultralytics.com/glossary/data-visualization) and analysis tools.
 
 ## YOLOv8 Training with DVCLive
 
@@ -78,7 +78,7 @@ yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=512
 yolo train model=yolov8n.pt data=coco8.yaml epochs=5 imgsz=640
 ```
 
-Adjust the model, data, epochs, and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
+Adjust the model, data, [epochs](https://www.ultralytics.com/glossary/epoch), and imgsz parameters according to your specific requirements. For a detailed understanding of the model training process and best practices, refer to our [YOLOv8 Model Training guide](../modes/train.md).
 
 ### Monitoring Experiments with DVCLive
 
@@ -108,7 +108,7 @@ df.reset_index(drop=True, inplace=True)
 print(df)
 ```
 
-The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLOv8 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's accuracy, with higher values indicating better performance.
+The output of the code snippet above provides a clear tabular view of the different experiments conducted with YOLOv8 models. Each row represents a different training run, detailing the experiment's name, the number of epochs, image size (imgsz), the specific model used, and the mAP50-95(B) metric. This metric is crucial for evaluating the model's [accuracy](https://www.ultralytics.com/glossary/accuracy), with higher values indicating better performance.
 
 #### Visualizing Results with Plotly
 
@@ -135,7 +135,7 @@ DVC provides a useful command to generate comparative plots for your experiments
 dvc plots diff $(dvc exp list --names-only)
 ```
 
-After executing this command, DVC generates plots comparing the metrics across different experiments, which are saved as HTML files. Below is an example image illustrating typical plots generated by this process. The image showcases various graphs, including those representing mAP, recall, precision, loss values, and more, providing a visual overview of key performance metrics:
+After executing this command, DVC generates plots comparing the metrics across different experiments, which are saved as HTML files. Below is an example image illustrating typical plots generated by this process. The image showcases various graphs, including those representing mAP, [recall](https://www.ultralytics.com/glossary/recall), [precision](https://www.ultralytics.com/glossary/precision), loss values, and more, providing a visual overview of key performance metrics:
 
 <p align="center">
   <img width="640" src="https://github.com/ultralytics/docs/releases/download/0/dvclive-comparative-plots.avif" alt="DVCLive Plots">
@@ -156,7 +156,7 @@ This code will render the HTML file containing the DVC plots directly in your Ju
 
 ### Making Data-Driven Decisions
 
-Use the insights gained from these visualizations to make informed decisions about model optimizations, hyperparameter tuning, and other modifications to enhance your model's performance.
+Use the insights gained from these visualizations to make informed decisions about model optimizations, [hyperparameter tuning](https://www.ultralytics.com/glossary/hyperparameter-tuning), and other modifications to enhance your model's performance.
 
 ### Iterating on Experiments