Updates with youtube links.

Author: rajivsam

README.md

@@ -15,7 +15,7 @@
 
 This tool is used for knowledge management in data science. As data scientists, incremental experimentation is a way of life. The problem is we have a lot of them and even small projects accumalate context, decisions and rationale over time. This is not a problem if we have both the need for experimentation (the design question or issue) and the results documented over time, but this tends to be done in an adhoc manner, so when its time to rebuild or revisit a particular question, we can't find the research and the results related to it. This is the need this tool fulfils.
 
-Please see [knowledge application development context](https://github.com/rajivsam/KMDS/blob/main/feature_documentation/knowledge_management_in_DS.md) for a description of a typical knowledge application development setting. Please see [the video](example_documentation/video/Knowledge_Management_for_Data_Science_comp.mp4) for a quick overview.
+Please see [knowledge application development context](https://github.com/rajivsam/KMDS/blob/main/feature_documentation/knowledge_management_in_DS.md) for a description of a typical knowledge application development setting. Please see [the video](https://www.youtube.com/watch?v=n7gE6bfLWtI) for a quick overview.
 
 ### How is it related to process guidelines and vocabularies for machine learning?
 Initiatives such as [CRISP DM](https://www.datascience-pm.com/crisp-dm-2/) provide guidelines and processes for developing data science projects. Projects such as [Open ML](https://openml.github.io/openml-python/main/index.html) provide semantic vocabulary standardization for machine learning tasks. These are excellent tools. However, the guidelines they provide are task focussed. The gap between a conceptual idea and the final, or, even candidate data science tasks for a project is filled with many assumptions and experimental evaluations. The information in these assumptions and experimental evaluations is what this tool aims to capture. There is also an ordering to these assumptions and experimental evaluations. This is also what this tool aims to capture.
@@ -31,8 +31,10 @@ This version of the tool takes all the recent advances (as of early 2026) into c
 3. As you work through your exploratory data analysis, data representation and modeling phases, log your findings to ```kmds```
 4. Run a report to fetch the details of your design rationale as needed. To communicate your findings to your team or management, simply export your knowledge base. Point a generative AI tool such as __NotebookLM__ to the export and generate your report, video or other documentation artifact.
 
+For a video excerpt of the design cosiderations, see [this video](https://www.youtube.com/watch?v=qRTsM6MNxIQ)
+
 ### Examples of use
-The repository contains two examples of use. One example is from analytics, the other is from machine learning. The notebooks for analytics are in [the analytics example](examples_of_use/analytics) and the notebooks for machine learning are in [the machine learning example](examples_of_use/machine_learning). The analytics example evaluates the effectiveness of ticket resolution help desk. Using ticket resolution data for a particular quarter, Q2 2016, the example illustrates how effectiveness of the organization can be evaluated. The reader can explore the notebooks to see the details of the implementation and details of how findings in each phase of the model building cycle are logged. The findings from the resulting knowledge base can be exported to create materials to communicate the details of the project to team members and management, see [this video](examples_of_use/analytics/Help_Desk_Analytics%20_comp.mp4) and this [infographic](examples_of_use/analytics/usecase_overview_mindmap.png)
+The repository contains two examples of use. One example is from analytics, the other is from machine learning. The notebooks for analytics are in [the analytics example](examples_of_use/analytics) and the notebooks for machine learning are in [the machine learning example](examples_of_use/machine_learning). The analytics example evaluates the effectiveness of ticket resolution help desk. Using ticket resolution data for a particular quarter, Q2 2016, the example illustrates how effectiveness of the organization can be evaluated. The reader can explore the notebooks to see the details of the implementation and details of how findings in each phase of the model building cycle are logged. The findings from the resulting knowledge base can be exported to create materials to communicate the details of the project to team members and management, see [this video](youtube.com/watch?v=zmm0O4_fK_c&feature=youtu.be) and this [infographic](examples_of_use/analytics/usecase_overview_mindmap.png)
 
 The machine learning example illustrates how Principal Components Analysis can be used to summarize the sales activity in an online store for a particular quarter. The reader can view the notebooks under [the machine learning example](examples_of_use/machine_learning) for details of the implementation. As with the analytics example, generative AI tools (Notebook LM in this case) can be used to communicate the findings and results from the knowledge base, see [this infographic](examples_of_use/machine_learning/ml_infographic_kmds.png).