diff --git a/5-Data-Science-In-Cloud/18-tbd/README.md b/5-Data-Science-In-Cloud/18-tbd/README.md
index abf837a1..d4238c0c 100644
--- a/5-Data-Science-In-Cloud/18-tbd/README.md
+++ b/5-Data-Science-In-Cloud/18-tbd/README.md
@@ -5,8 +5,9 @@ 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)
+    - [1.1 What is Azure Machine Learning?](#11-what-is-azure-machine-learning)
+    - [1.2 The Heart Failure Prediction Project](#12-the-heart-failure-prediction-project)
+    - [1.3 The Heart Failure Dataset](#13-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)
@@ -25,8 +26,25 @@ Table of contents:
 
 [Pre-lecture quiz]()
 ## 1. Introduction
-### 1.1 The Heart Failure Prediction Project
-### 1.2 The Heart Failure Dataset
+### 1.1 What is Azure Machine Learning?
+Data scientists expend a lot of effort exploring and pre-processing data, and trying various types of model-training algorithms to produce accurate models, which is time consuming, and often makes inefficient use of expensive compute hardware.
+
+[Azure ML](https://docs.microsoft.com/EN-US/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud-based platform for building and operating machine learning solutions in Azure. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage. Most importantly, it helps data scientists increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively to handle large volumes of data while incurring costs only when actually used.
+
+Azure ML provides all the tools developers and data scientists need for their machine learning workflows, including:
+
+- **Azure Machine Learning Studio** is a web portal in Azure Machine Learning for low-code and no-code options for model training, deployment, automation, tracking and asset management. The studio integrates with the Azure Machine Learning SDK for a seamless experience.
+- **Jupyter Notebooks** to quickly prototype and test ML models
+- **Azure Machine Learning Designer** allows to drag-n-drop modules to build experiments and then deploy pipelines in a low-code environment.
+- **Automated machine learning UI (AutoML)** automates iterative tasks of machine learning model development allowing to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
+- **Data labeling**: an assisted ML tool to automatically label data.
+- **Machine learning extension for Visual Studio Code** provides a full-featured development environment for building and managing ML projects.
+- **Machine learning CLI** provides commands for managing Azure ML resources from the command line.
+- **Integration with open-source frameworks** such as PyTorch, TensorFlow, and scikit-learn and many more for training, deploying, and managing the end-to-end machine learning process.
+- **MLflow** is an open-source library for managing the life cycle of your machine learning experiments. **MLFlow Tracking** is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment.
+
+### 1.2 The Heart Failure Prediction Project
+### 1.3 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