Merge pull request #5 from dmaliugina/patch-3-fixes

Module 1 fixes
evidentlyai · Oct 12, 2023 · 8005b3d · 8005b3d
2 parents ae1de89 + 80091d7
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diff --git a/...ok/ml-observability-course/module-1-introduction/ml-monitoring-architectures.md b/...ok/ml-observability-course/module-1-introduction/ml-monitoring-architectures.md
@@ -36,4 +36,4 @@ When it comes to visualizing the results of monitoring, you also have options.
 
 Each ML monitoring architecture has its pros and cons. When choosing between them, consider existing tools, the scale of ML deployments, and available team resources for systems support. Be pragmatic: you can start with a simpler architecture and expand later. 
 
-For a deeper dive into the ML monitoring architectures with specific code examples, head to [Module 5](ml-observability-course/module-5-ml-pipelines-validation-and-testing.md) and [Module 6](ml-observability-course/module-6-deploying-an-ml-monitoring-dashboard.md). 
+For a deeper dive into the ML monitoring architectures with specific code examples, head to [Module 5](../module-5-ml-pipelines-validation-and-testing.md) and [Module 6](../module-6-deploying-an-ml-monitoring-dashboard.md). 
diff --git a/docs/book/ml-observability-course/module-1-introduction/ml-monitoring-metrics.md b/docs/book/ml-observability-course/module-1-introduction/ml-monitoring-metrics.md
@@ -27,4 +27,4 @@ The ultimate measure of the model quality is its impact on the business. Dependi
 
 ![](<../../../images/2023109\_course\_module1\_fin\_images.034.png>)
 
-For a deeper dive into **ML model quality and relevance** and **data quality and integrity** metrics, head to [Module 2](ml-observability-course/module-2-ml-monitoring-metrics.md).
+For a deeper dive into **ML model quality and relevance** and **data quality and integrity** metrics, head to [Module 2](../module-2-ml-monitoring-metrics.md).
diff --git a/docs/book/ml-observability-course/module-1-introduction/ml-monitoring-setup.md b/docs/book/ml-observability-course/module-1-introduction/ml-monitoring-setup.md
@@ -58,4 +58,4 @@ While designing an ML monitoring system, tailor your approach to fit your specif
 * Use reference datasets to simplify the monitoring process but make sure they are carefully curated.
 * Define custom metrics that fit your problem statement and data properties.
 
-For a deeper dive into the ML monitoring setup, head to [Module 4](ml-observability-course/module-4-designing-effective-ml-monitoring.md).
+For a deeper dive into the ML monitoring setup, head to [Module 4](../module-4-designing-effective-ml-monitoring.md).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -36,4 +36,4 @@ When it comes to visualizing the results of monitoring, you also have options.

		Each ML monitoring architecture has its pros and cons. When choosing between them, consider existing tools, the scale of ML deployments, and available team resources for systems support. Be pragmatic: you can start with a simpler architecture and expand later.

		For a deeper dive into the ML monitoring architectures with specific code examples, head to [Module 5](ml-observability-course/module-5-ml-pipelines-validation-and-testing.md) and [Module 6](ml-observability-course/module-6-deploying-an-ml-monitoring-dashboard.md).
		For a deeper dive into the ML monitoring architectures with specific code examples, head to [Module 5](../module-5-ml-pipelines-validation-and-testing.md) and [Module 6](../module-6-deploying-an-ml-monitoring-dashboard.md).
Original file line number	Diff line number	Diff line change
Expand Up		@@ -27,4 +27,4 @@ The ultimate measure of the model quality is its impact on the business. Dependi

		![](<../../../images/2023109\_course\_module1\_fin\_images.034.png>)

		For a deeper dive into ML model quality and relevance and data quality and integrity metrics, head to [Module 2](ml-observability-course/module-2-ml-monitoring-metrics.md).
		For a deeper dive into ML model quality and relevance and data quality and integrity metrics, head to [Module 2](../module-2-ml-monitoring-metrics.md).