diff --git a/5-Data-Science-In-Cloud/18-tbd/README.md b/5-Data-Science-In-Cloud/18-tbd/README.md
index 70b5f3a..abf837a 100644
--- a/5-Data-Science-In-Cloud/18-tbd/README.md
+++ b/5-Data-Science-In-Cloud/18-tbd/README.md
@@ -1,9 +1,42 @@
-# Data Science in the Cloud: TBD
-
+# Low code/No code Data Science in the Cloud
+
+Table of contents:
+
+- [Low code/No code Data Science in the Cloud](#low-codeno-code-data-science-in-the-cloud)
+  - [Pre-Lecture Quiz](#pre-lecture-quiz)
+  - [1. Introduction](#1-introduction)
+    - [1.1 The Heart Failure Prediction Project](#11-the-heart-failure-prediction-project)
+    - [1.2 The Heart Failure Dataset](#12-the-heart-failure-dataset)
+  - [2. Low code/No code training of a model in Azure ML Studio](#2-low-codeno-code-training-of-a-model-in-azure-ml-studio)
+    - [2.1 Create an Azure ML workspace](#21-create-an-azure-ml-workspace)
+    - [2.2 Compute Resources](#22-compute-resources)
+      - [2.2.1 Choosing the right options for your compute resources](#221-choosing-the-right-options-for-your-compute-resources)
+      - [2.2.2 Creating a compute cluster](#222-creating-a-compute-cluster)
+    - [2.3 Loading the Dataset](#23-loading-the-dataset)
+    - [2.4 Low code/No Code training with AutoML](#24-low-codeno-code-training-with-automl)
+  - [3. Low code/No Code model deployment and endpoint consumption](#3-low-codeno-code-model-deployment-and-endpoint-consumption)
+    - [3.1 Model deployment](#31-model-deployment)
+    - [3.2 Endpoint consumption](#32-endpoint-consumption)
+  - [🚀 Challenge](#-challenge)
+  - [Post-Lecture Quiz](#post-lecture-quiz)
+  - [Review & Self Study](#review--self-study)
+  - [Assignment](#assignment)
 ## Pre-Lecture Quiz
 
 [Pre-lecture quiz]()
-
+## 1. Introduction
+### 1.1 The Heart Failure Prediction Project
+### 1.2 The Heart Failure Dataset
+## 2. Low code/No code training of a model in Azure ML Studio
+### 2.1 Create an Azure ML workspace
+### 2.2 Compute Resources
+#### 2.2.1 Choosing the right options for your compute resources
+#### 2.2.2 Creating a compute cluster
+### 2.3 Loading the Dataset
+### 2.4 Low code/No Code training with AutoML 
+## 3. Low code/No Code model deployment and endpoint consumption
+### 3.1 Model deployment
+### 3.2 Endpoint consumption
 ## 🚀 Challenge
 
 
diff --git a/5-Data-Science-In-Cloud/README.md b/5-Data-Science-In-Cloud/README.md
index 92cfb83..9198d9f 100644
--- a/5-Data-Science-In-Cloud/README.md
+++ b/5-Data-Science-In-Cloud/README.md
@@ -5,7 +5,7 @@ Now we will see why and how you can use Cloud services for Data Science.
 ### Topics
 
 1. [Why do Data Science in the Cloud?](17-tbd/README.md)
-2. [Optimizing a Data Science Pipeline in Azure](18-tbd/README.md)
+2. [Low code/No code Data Science in the cloud](18-tbd/README.md)
 3. [Machine Learning Operations in the Cloud (MLOps)](19-tbd/README.md)
 
 ### Credits