Add Docs glossary links (#16448)

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

docs/en/guides/model-monitoring-and-maintenance.md

@@ -34,8 +34,8 @@ Here are some best practices to keep in mind while monitoring your computer visi
 You can use automated monitoring tools to make it easier to monitor models after deployment. Many tools offer real-time insights and alerting capabilities. Here are some examples of open-source model monitoring tools that can work together:
 
 - **[Prometheus](https://prometheus.io/)**: Prometheus is an open-source monitoring tool that collects and stores metrics for detailed performance tracking. It integrates easily with Kubernetes and Docker, collecting data at set intervals and storing it in a time-series database. Prometheus can also scrape HTTP endpoints to gather real-time metrics. Collected data can be queried using the PromQL language.
-- **[Grafana](https://grafana.com/)**: Grafana is an open-source data visualization and monitoring tool that allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. It works well with Prometheus and offers advanced data visualization features. You can create custom dashboards to show important metrics for your computer vision models, like inference latency, error rates, and resource usage. Grafana turns collected data into easy-to-read dashboards with line graphs, heat maps, and histograms. It also supports alerts, which can be sent through channels like Slack to quickly notify teams of any issues.
-- **[Evidently AI](https://www.evidentlyai.com/)**: Evidently AI is an open-source tool designed for monitoring and debugging machine learning models in production. It generates interactive reports from pandas DataFrames, helping analyze machine learning models. Evidently AI can detect data drift, model performance degradation, and other issues that may arise with your deployed models.
+- **[Grafana](https://grafana.com/)**: Grafana is an open-source [data visualization](https://www.ultralytics.com/glossary/data-visualization) and monitoring tool that allows you to query, visualize, alert on, and understand your metrics no matter where they are stored. It works well with Prometheus and offers advanced data visualization features. You can create custom dashboards to show important metrics for your computer vision models, like inference latency, error rates, and resource usage. Grafana turns collected data into easy-to-read dashboards with line graphs, heat maps, and histograms. It also supports alerts, which can be sent through channels like Slack to quickly notify teams of any issues.
+- **[Evidently AI](https://www.evidentlyai.com/)**: Evidently AI is an open-source tool designed for monitoring and debugging [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) models in production. It generates interactive reports from pandas DataFrames, helping analyze machine learning models. Evidently AI can detect data drift, model performance degradation, and other issues that may arise with your deployed models.
 
 The three tools introduced above, Evidently AI, Prometheus, and Grafana, can work together seamlessly as a fully open-source ML monitoring solution that is ready for production. Evidently AI is used to collect and calculate metrics, Prometheus stores these metrics, and Grafana displays them and sets up alerts. While there are many other tools available, this setup is an exciting open-source option that provides robust capabilities for monitoring and maintaining your models.
 
@@ -45,7 +45,7 @@ The three tools introduced above, Evidently AI, Prometheus, and Grafana, can wor
 
 ### Anomaly Detection and Alert Systems
 
-An anomaly is any data point or pattern that deviates quite a bit from what is expected. With respect to computer vision models, anomalies can be images that are very different from the ones the model was trained on. These unexpected images can be signs of issues like changes in data distribution, outliers, or behaviors that might reduce model performance. Setting up alert systems to detect these anomalies is an important part of model monitoring.
+An anomaly is any data point or pattern that deviates quite a bit from what is expected. With respect to [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) models, anomalies can be images that are very different from the ones the model was trained on. These unexpected images can be signs of issues like changes in data distribution, outliers, or behaviors that might reduce model performance. Setting up alert systems to detect these anomalies is an important part of model monitoring.
 
 By setting standard performance levels and limits for key metrics, you can catch problems early. When performance goes outside these limits, alerts are triggered, prompting quick fixes. Regularly updating and retraining models with new data keeps them relevant and accurate as the data changes.
 
@@ -69,7 +69,7 @@ Here are several methods to detect data drift:
 
 **Continuous Monitoring**: Regularly monitor the model's input data and outputs for signs of drift. Track key metrics and compare them against historical data to identify significant changes.
 
-**Statistical Techniques**: Use methods like the Kolmogorov-Smirnov test or Population Stability Index (PSI) to detect changes in data distributions. These tests compare the distribution of new data with the training data to identify significant differences.
+**Statistical Techniques**: Use methods like the Kolmogorov-Smirnov test or Population Stability Index (PSI) to detect changes in data distributions. These tests compare the distribution of new data with the [training data](https://www.ultralytics.com/glossary/training-data) to identify significant differences.
 
 **Feature Drift**: Monitor individual features for drift. Sometimes, the overall data distribution may remain stable, but individual features may drift. Identifying which features are drifting helps in fine-tuning the retraining process.
 
@@ -91,7 +91,7 @@ Regardless of the method, validation and testing are a must after updates. It is
 
 ### Deciding When to Retrain Your Model
 
-The frequency of retraining your computer vision model depends on data changes and model performance. Retrain your model whenever you observe a significant performance drop or detect data drift. Regular evaluations can help determine the right retraining schedule by testing the model against new data. Monitoring performance metrics and data patterns lets you decide if your model needs more frequent updates to maintain accuracy.
+The frequency of retraining your computer vision model depends on data changes and model performance. Retrain your model whenever you observe a significant performance drop or detect data drift. Regular evaluations can help determine the right retraining schedule by testing the model against new data. Monitoring performance metrics and data patterns lets you decide if your model needs more frequent updates to maintain [accuracy](https://www.ultralytics.com/glossary/accuracy).
 
 <p align="center">
   <img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/when-to-retrain-overview.avif" alt="When to Retrain Overview">
@@ -108,8 +108,8 @@ These are some of the key elements that should be included in project documentat
 - **[Project Overview](./steps-of-a-cv-project.md)**: Provide a high-level summary of the project, including the problem statement, solution approach, expected outcomes, and project scope. Explain the role of computer vision in addressing the problem and outline the stages and deliverables.
 - **Model Architecture**: Detail the structure and design of the model, including its components, layers, and connections. Explain the chosen hyperparameters and the rationale behind these choices.
 - **[Data Preparation](./data-collection-and-annotation.md)**: Describe the data sources, types, formats, sizes, and preprocessing steps. Discuss data quality, reliability, and any transformations applied before training the model.
-- **[Training Process](./model-training-tips.md)**: Document the training procedure, including the datasets used, training parameters, and loss functions. Explain how the model was trained and any challenges encountered during training.
-- **[Evaluation Metrics](./model-evaluation-insights.md)**: Specify the metrics used to evaluate the model's performance, such as accuracy, precision, recall, and F1-score. Include performance results and an analysis of these metrics.
+- **[Training Process](./model-training-tips.md)**: Document the training procedure, including the datasets used, training parameters, and [loss functions](https://www.ultralytics.com/glossary/loss-function). Explain how the model was trained and any challenges encountered during training.
+- **[Evaluation Metrics](./model-evaluation-insights.md)**: Specify the metrics used to evaluate the model's performance, such as accuracy, [precision](https://www.ultralytics.com/glossary/precision), [recall](https://www.ultralytics.com/glossary/recall), and F1-score. Include performance results and an analysis of these metrics.
 - **[Deployment Steps](./model-deployment-options.md)**: Outline the steps taken to deploy the model, including the tools and platforms used, deployment configurations, and any specific challenges or considerations.
 - **Monitoring and Maintenance Procedure**: Provide a detailed plan for monitoring the model's performance post-deployment. Include methods for detecting and addressing data and model drift, and describe the process for regular updates and retraining.
 
@@ -155,7 +155,7 @@ Maintaining computer vision models involves regular updates, retraining, and mon
 
 Data drift detection is essential because it helps identify when the statistical properties of the input data change over time, which can degrade model performance. Techniques like continuous monitoring, statistical tests (e.g., Kolmogorov-Smirnov test), and feature drift analysis can help spot issues early. Addressing data drift ensures that your model remains accurate and relevant in changing environments. Learn more about data drift detection in our [Data Drift Detection](#data-drift-detection) section.
 
-### What tools can I use for anomaly detection in computer vision models?
+### What tools can I use for [anomaly detection](https://www.ultralytics.com/glossary/anomaly-detection) in computer vision models?
 
 For anomaly detection in computer vision models, tools like [Prometheus](https://prometheus.io/), [Grafana](https://grafana.com/), and [Evidently AI](https://www.evidentlyai.com/) are highly effective. These tools can help you set up alert systems to detect unusual data points or patterns that deviate from expected behavior. Configurable alerts and standardized messages can help you respond quickly to potential issues. Explore more in our [Anomaly Detection and Alert Systems](#anomaly-detection-and-alert-systems) section.
 
@@ -168,5 +168,5 @@ Effective documentation of a computer vision project should include:
 - **Data Preparation**: Information on data sources, preprocessing steps, and transformations.
 - **Training Process**: Description of the training procedure, datasets used, and challenges encountered.
 - **Evaluation Metrics**: Metrics used for performance evaluation and analysis.
-- **Deployment Steps**: Steps taken for model deployment and any specific challenges.
+- **Deployment Steps**: Steps taken for [model deployment](https://www.ultralytics.com/glossary/model-deployment) and any specific challenges.
 - **Monitoring and Maintenance Procedure**: Plan for ongoing monitoring and maintenance. For more comprehensive guidelines, refer to our [Documentation](#documentation) section.