Final updates

Author: svpino

.guide/introduction-to-the-program/introduction.md

@@ -0,0 +1,16 @@
+# Introduction
+
+"Building Machine Learning Systems" is designed to teach you how to train, evaluate, deploy, and monitor machine learning models in production.
+
+In this repository, you'll find a fully-fledged, end-to-end machine learning system you can use as a starting point for your projects. This template uses the [Penguins dataset](https://www.kaggle.com/parulpandey/palmer-archipelago-antarctica-penguin-data) to train a model capable of classifying penguin species. 
+
+![Penguins dataset](.guide/introduction/images/penguins.png)
+
+This guide provides instructions on how to implement each component of the solution and how to deploy and run the system locally and on different cloud platforms.
+
+Here is a high-level architecture diagram showing the main components of the solution:
+
+![System architecture](.guide/introduction/images/architecture.png)
+
+If you find any issues with the code or have any ideas for improving it, please reach out with your recommendations. 
+

.guide/introduction-to-the-program/eda.md .guide/introduction/eda.md

@@ -8,7 +8,7 @@ The command below starts a local server listening in port `8080`. This server wi
 just serve
 ```
 
-You can see the actual command behind the `serve` recipe by opening the [`justfile`](/justfile) file. Notice how the command uses the `MLFLOW_TRACKING_URI` environment variable to get the latest version of the model from the model registry. Review the [Environment variables](.guide/introduction-to-the-program/env.md) section to learn more about the environment variables used in the project. 
+You can see the actual command behind the `serve` recipe by opening the [`justfile`](/justfile) file. Notice how the command uses the `MLFLOW_TRACKING_URI` environment variable to get the latest version of the model from the model registry. Review the [Environment variables](.guide/introduction/env.md) section to learn more about the environment variables used in the project. 
 
 If we want the model to capture the input data and the predictions it generates, we must specify a backend implementation using the `MODEL_BACKEND` environment variable. You can do that by running the following command:
 

.guide/introduction-to-the-program/env.md .guide/introduction/env.md

@@ -1,19 +1,15 @@
 [
 	{
-		"label": "Introduction to the program",
-		"description": "Overview of the program and instructions on how to navigate the codebase.",
-		"markdown": ".guide/introduction-to-the-program/introduction.md",
+		"label": "Introduction",
+		"description": "System overview and high-level architecture.",
+		"markdown": ".guide/introduction/introduction.md",
 		"actions": [
-			{
-				"label": "Check Python version",
-				"target": "just python"
-			}
 		],
 		"lessons": [
 			{
 				"label": "Preparing your environment",
 				"description": "Setting up your development environment and installing the required dependencies.",
-				"markdown": ".guide/introduction-to-the-program/setup.md",
+				"markdown": ".guide/introduction/setup.md",
 				"actions": [
 					{
 						"label": "Check dependencies",
@@ -24,7 +20,7 @@
 			{
 				"label": "Setting up MLflow",
 				"description": "Configuring MLflow to track experiments, manage models, and streamline the training process.",
-				"markdown": ".guide/introduction-to-the-program/mlflow.md",
+				"markdown": ".guide/introduction/mlflow.md",
 				"actions": [
 					{
 						"label": "Run MLflow server",
@@ -41,7 +37,7 @@
 			{
 				"label": "Environment variables",
 				"description": "Setting up the necessary environment variables to configure the pipelines.",
-				"markdown": ".guide/introduction-to-the-program/env.md",
+				"markdown": ".guide/introduction/env.md",
 				"actions": [
 					{
 						"label": "Set up .env file",
@@ -53,7 +49,7 @@
 				"label": "Exploratory data analysis",
 				"description": "Exploring the dataset to understand its features and the relationships between them.",
 				"file": "notebooks/eda.ipynb",
-				"markdown": ".guide/introduction-to-the-program/eda.md",
+				"markdown": ".guide/introduction/eda.md",
 				"actions": [
 					{
 						"label": "Execute notebook",