docs: Update docs

Author: iusztinpaul

.env.example

@@ -1,6 +1,5 @@
-# Hopsworks API config
+# Your Hopsworks API key
 HOPSWORKS_API_KEY=
 
-# OpenAI API config
-OPENAI_MODEL_ID=gpt-4o-mini
-OPENAI_API_KEY=
+# Your OpenAI API key (Optional - required only for the last LLM lesson)
+OPENAI_API_KEY= 

INSTALL_AND_USAGE.md

@@ -26,18 +26,29 @@ This will:
 
 # Usage
 
-## Pipeline Components
+## Set environment variables
+
+The first step before running anything is to set up the environment variables required to access Hopsworks and OpenAI's API.
+
+Go to the root of the repository and run:
+```bash
+cp .env.example .env
+```
+
+Open your `.env` file and fill it as explain in the comments.
+
+## Pipeline components
 
 The project consists of several pipeline components that can be run individually or all at once.
 
-### Running the Complete Pipeline
+### Running all the pipelines at once
 
-To run all pipeline components in sequence:
+To run all the pipeline at once in a sequence, run:
 ```bash
 make all
 ```
 
-This will execute the following steps in order:
+This will execute all the ML pipelines in the following order:
 1. Feature Engineering
 2. Retrieval Model Training
 3. Ranking Model Training
@@ -51,43 +62,41 @@ You can also run each component separately:
 
 ### Individual Pipeline Components
 
-You can also run each component separately:
-
 1. **Feature Engineering**
+   Execute the feature engineering Notebook (`notebooks/1_fp_computing_features.ipynb`):
    ```bash
    make feature-engineering
    ```
-   Executes the feature engineering notebook (`notebooks/1_fp_computing_features.ipynb`)
 
 2. **Train Retrieval Model**
+   Execute the retrieval model training Notebook (`notebooks/2_tp_training_retrieval_model.ipynb`):
    ```bash
    make train-retrieval
    ```
-   Trains the retrieval model using `notebooks/2_tp_training_retrieval_model.ipynb`
 
 3. **Train Ranking Model**
+   Execute the ranking model training Notebook (`notebooks/3_tp_training_ranking_model.ipynb`):
    ```bash
    make train-ranking
    ```
-   Trains the ranking model using `notebooks/3_tp_training_ranking_model.ipynb`
 
 4. **Create Embeddings**
+   Execute the embeddings computation Notebook (`notebooks/4_fp_computing_item_embeddings.ipynb`):
    ```bash
    make create-embeddings
    ```
-   Generates embeddings using `notebooks/4_fp_computing_item_embeddings.ipynb`
 
 5. **Create Deployments**
+   Execute the deployments creation Notebook (`notebooks/5_ip_creating_deployments.ipynb`):
    ```bash
    make create-deployments
    ```
-   Sets up model deployments using `notebooks/5_ip_creating_deployments.ipynb`
 
 6. **Schedule Materialization Jobs**
+   Execute the materialization jobs scheduling Notebook (`notebooks/6_scheduling_materialization_jobs.ipynb`):
    ```bash
    make schedule-materialization-jobs
    ```
-   Schedules materialization jobs using `notebooks/6_scheduling_materialization_jobs.ipynb`
 
 ### Notes
 - All notebooks are executed using IPython through the UV virtual environment
@@ -96,14 +105,16 @@ You can also run each component separately:
 
 ## Run Streamlit app
 
-To launch the Streamlit application that uses the feature store and fine-tuned models, run:
+To launch the Streamlit frontend application that uses the feature store and fine-tuned models, run:
 
 ```bash
 make start-ui
 ```
 
 ## Clean Hopsworks resources
 
+To clean all 
+
 ```bash
 make clean-hopsworks-resources
 ```

README.md

@@ -10,13 +10,50 @@
   </a>
 </p>
 
+## What will you learn?
+
 The **Hands-on H&M Real-Time Personalized Recommender”** is a free course that will teach you how to build and deploy a real-time personalized recommender for H&M fashion articles using the 4-stage recommender architecture, the two-tower model design and the Hopsworks AI Lakehouse. 
 
+You will learn:
+
+- building a recommender using the 4-stage recommender architecture
+- training a two-tower model for generating users and item embeddings
+- designing a scalable ML system using the FTI architecture
+- using MLOps best practices such as a feature store and model registry
+- deploying the real-time personalized recommender
+- enhancing recommendations with LLMs
+- implementing an interactive web interface
+
+## Who is this for?
+
+## Costs?
+
+## How will you learn?
+
+## Questions and troubleshooting
+
 ## Lessons
 
-* [Lesson 1: Building a TikTok-like recommender](https://decodingml.substack.com/p/33d3273e-b8e3-4d98-b160-c3d239343022)
-* Lesson 2: The feature pipeline (WIP)
-* Lesson 3: The training pipeline (WIP)
-* Lesson 4: The inference pipeline (WIP)
-* Lesson 5: Building real-time recommenders with LLMs (WIP)
+| Lesson | Title | Description | Local Notebooks | Colab Notebooks |
+|--------|-------|-------------|----------------|-----------------|
+| 1 | [Building a TikTok-like recommender](https://decodingml.substack.com/p/33d3273e-b8e3-4d98-b160-c3d239343022) | Learn how to architect a recommender system using the 4-stage architecture and two-tower model. | - | - |
+| 2 | The feature pipeline | Learn how to build a scalable feature pipeline (WIP) | [1_fp_computing_features.ipynb](notebooks/1_fp_computing_features.ipynb) | - |
+| 3 | The training pipeline | Learn how to train and evaluate recommendation models (WIP) | [2_tp_training_retrieval_model.ipynb](notebooks/2_tp_training_retrieval_model.ipynb), [3_tp_training_ranking_model.ipynb](notebooks/3_tp_training_ranking_model.ipynb) | - |
+| 4 | The inference pipeline | Learn how to deploy models for real-time inference (WIP) | [4_fp_computing_item_embeddings.ipynb](notebooks/4_fp_computing_item_embeddings.ipynb),  [5_ip_creating_deployments.ipynb](notebooks/5_ip_creating_deployments.ipynb) | - |
+| 5 | Building personalized real-time recommenders with LLMs | Learn how to enhance recommendations with LLMs (WIP) | - | - |
+
+## Folder structure
+
+## Install and usage
+
+To understand how to install and run the code, go to the [INSTALL_AND_USAGE]() dedicated document.
+
+> [!Note]
+> Even though you can run everything solely using the INSTALL_AND_USAGE dedicated document, we recommend that you read the articles to understand how the personalized recommender works fully.
+
+## License
+
+This course is an open-source project released under the Apache-2.0 license. Thus, as long you distribute our LICENSE and acknowledge your project is based on our work, you can safely clone or fork this project and use it as a source of inspiration for your educational projects (e.g., university, college degree, personal projects, etc.).
+
+## Sponsors