Merge pull request #7 from dmaliugina/patch-4-module2-fixes

Module 2 fixes
evidentlyai · Oct 12, 2023 · c310691 · c310691
2 parents dfc776f + 6a91f58
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diff --git a/docs/book/README.md b/docs/book/README.md
@@ -34,8 +34,8 @@ ML observability course is organized into six modules. You can follow the comple
 [Module 1. Introduction to ML monitoring and observability](ml-observability-course/module-1-introduction/readme.md). 
 {% endcontent-ref %}
 
-{% content-ref url="ml-observability-course/module-2-ml-monitoring-metrics.md" %}
-[Module 2. ML monitoring metrics: model quality, data quality, data drift](ml-observability-course/module-2-ml-monitoring-metrics.md). 
+{% content-ref url="ml-observability-course/module-2-ml-monitoring-metrics/readme.md" %}
+[Module 2. ML monitoring metrics: model quality, data quality, data drift](ml-observability-course/module-2-ml-monitoring-metrics/readme.md). 
 {% endcontent-ref %}
 
 {% content-ref url="ml-observability-course/module-3-ml-monitoring-for-unstructured-data.md" %}

diff --git a/docs/book/SUMMARY.md b/docs/book/SUMMARY.md
@@ -13,11 +13,11 @@
 * [Module 2: ML monitoring metrics](ml-observability-course/module-2-ml-monitoring-metrics/readme.md)
   * [2.1. How to evaluate ML model quality](ml-observability-course/module-2-ml-monitoring-metrics/evaluate-ml-model-quality.md)
   * [2.2. Overview of ML quality metrics. Classification, regression, ranking](ml-observability-course/module-2-ml-monitoring-metrics/ml-quality-metrics-classification-regression-ranking.md)
-  * [2.3. Evaluating ML model quality CODE PRACTICE](ml-observability-course/module-2-ml-monitoring-metrics/ml-model-quality-code-practice.md)
+  * [2.3. Evaluating ML model quality [CODE PRACTICE]](ml-observability-course/module-2-ml-monitoring-metrics/ml-model-quality-code-practice.md)
   * [2.4. Data quality in machine learning](ml-observability-course/module-2-ml-monitoring-metrics/data-quality-in-ml.md)
-  * [2.5. Data quality in ML CODE PRACTICE](ml-observability-course/module-2-ml-monitoring-metrics/data-quality-code-practice.md)
+  * [2.5. Data quality in ML [CODE PRACTICE]](ml-observability-course/module-2-ml-monitoring-metrics/data-quality-code-practice.md)
   * [2.6. Data and prediction drift in ML](ml-observability-course/module-2-ml-monitoring-metrics/data-prediction-drift-in-ml.md)
-  * [2.8. Data and prediction drift in ML CODE PRACTICE](ml-observability-course/module-2-ml-monitoring-metrics/data-prediction-drift-code-practice.md)
+  * [2.8. Data and prediction drift in ML [CODE PRACTICE]](ml-observability-course/module-2-ml-monitoring-metrics/data-prediction-drift-code-practice.md)
 * [Module 3: ML monitoring for unstructured data](ml-observability-course/module-3-ml-monitoring-for-unstructured-data.md)
 * [Module 4: Designing effective ML monitoring](ml-observability-course/module-4-designing-effective-ml-monitoring.md)
 * [Module 5: ML pipelines validation and testing](ml-observability-course/module-5-ml-pipelines-validation-and-testing.md)