From 84d4da06796b650647d7025d0cd27ce2e59a5dcc Mon Sep 17 00:00:00 2001
From: Dasha Maliugina <105814287+dmaliugina@users.noreply.github.com>
Date: Mon, 16 Oct 2023 15:34:44 -0300
Subject: [PATCH] Update README.md
Added course calendar and deadlines
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docs/book/README.md | 14 ++++++++++++++
1 file changed, 14 insertions(+)
diff --git a/docs/book/README.md b/docs/book/README.md
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@@ -58,6 +58,20 @@ ML observability course is organized into six modules. You can follow the comple
[Module 6. Deploying an ML monitoring dashboard](ml-observability-course/module-6-deploying-an-ml-monitoring-dashboard.md).
{% endcontent-ref %}
+# Course calendar and deadlines
+
+We will publish new materials throughout the course.
+
+| Module | Week |
+|--------------------------------------------------------------------------|---------------------------------------------------------------|
+| [Module 1: Introduction to ML monitoring and observability](https://learn.evidentlyai.com/ml-observability-course/module-1-introduction) | October 16, 2023 |
+| [Module 2: ML monitoring metrics: model quality, data quality, data drift](https://learn.evidentlyai.com/ml-observability-course/module-2-ml-monitoring-metrics) | October 23, 2023 |
+| [Module 3: ML monitoring for unstructured data: NLP, LLM and embeddings](https://learn.evidentlyai.com/ml-observability-course/module-3-ml-monitoring-for-unstructured-data) | October 30, 2023 |
+| [Module 4: Designing effective ML monitoring](https://learn.evidentlyai.com/ml-observability-course/module-4-designing-effective-ml-monitoring) | November 6, 2023 |
+| [Module 5: ML pipelines validation and testing](https://learn.evidentlyai.com/ml-observability-course/module-5-ml-pipelines-validation-and-testing) | November 13, 2023 |
+| [Module 6: Deploying an ML monitoring dashboard](https://learn.evidentlyai.com/ml-observability-course/module-6-deploying-an-ml-monitoring-dashboard) | November 20, 2023 |
+| Final assignment | November 27, 2023
Quizzes and assignment due December 1, 2023 |
+
# Our approach
* **Blend of theory and practice**. The course combines key concepts of ML observability and monitoring with practice-oriented tasks.
* **Practical code examples**. We provide end-to-end deployment blueprints and walk you through the code examples.