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Module 2 fixes #7

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4 changes: 2 additions & 2 deletions docs/book/README.md
Original file line number Diff line number Diff line change
Expand Up @@ -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" %}
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6 changes: 3 additions & 3 deletions docs/book/SUMMARY.md
Original file line number Diff line number Diff line change
Expand Up @@ -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)
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