docs: Update docs

Author: iusztinpaul

.gitignore

@@ -123,6 +123,7 @@ celerybeat.pid
 
 # Environments
 .env
+.env.old
 .venv
 env/
 venv/

INSTALL_AND_USAGE.md

@@ -68,6 +68,8 @@ You can also run each component separately:
    make feature-engineering
    ```
 
+   Go to Hopsworks
+
 2. **Train Retrieval Model**
    Execute the retrieval model training Notebook (`notebooks/2_tp_training_retrieval_model.ipynb`):
    ```bash

README.md

@@ -12,25 +12,44 @@
 
 ## 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. 
+The goal of this course is to teach you how to build and deploy a real-time personalized recommender for H&M fashion articles, going through:
 
-📙 **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
+- 4-stage recommender architecture
+- Two-tower model training
+- Scalable ML system design
+- MLOps best practices
+- Real-time deployment
+- LLM-enhanced recommendations
+- Interactive web interface
 
 ## Who is this for?
 
+ML/AI engineers looking to understand how to design, build and deploy real-time personalized recommenders. Also, it is a good fit for DE/DS/SWE who want to understand the engineering behind a recommender.
+
+This is NOT a course for data scientists or researchers showing how to train the most accurate models. This course will focus mostly on engineering and end-to-end system using MLOps best practices.
+
 ## Costs?
 
+The lessons are completely free. Also, we will stick to the free version of all the tools used throughout the course.
+
+The only thing that will cost you ~$1-2 is running the latest lesson on building recommenders with LLMs where we will use the OpenAI API. 
+
+To conclude, Lesson 1,2,3, and 4 are free and Lesson 5 will cost you ~$1-2, which you can choose not to run.
+
 ## How will you learn?
 
-## Questions and troubleshooting
+This is a self-paced course where we provide 5 lessons, which will go over the theory, system design and implementation of building a real-time personalized recommender. Everything is backed by the open-source code found in this repository.
+
+## Lessons
+
+| 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) | [![Open In Jupyter](https://img.shields.io/badge/Jupyter-Open%20Notebook-blue?logo=Jupyter)](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_ip_computing_item_embeddings.ipynb](notebooks/4_ip_computing_item_embeddings.ipynb),  [5_ip_creating_deployments.ipynb](notebooks/5_ip_creating_deployments.ipynb), [6_scheduling_materialization_jobs.ipynb](notebooks/6_scheduling_materialization_jobs.ipynb) | - |
+| 5 | Building personalized real-time recommenders with LLMs | Learn how to enhance recommendations with LLMs (WIP) | - | - |
+
 
 ## Project structure
 
@@ -50,27 +69,19 @@ At Decoding ML we teach how to build production ML systems, thus the course foll
 ├── pyproject.toml      # Project dependencies
 ```
 
-## Lessons
-
-| 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_ip_computing_item_embeddings.ipynb](notebooks/4_ip_computing_item_embeddings.ipynb),  [5_ip_creating_deployments.ipynb](notebooks/5_ip_creating_deployments.ipynb), [6_scheduling_materialization_jobs.ipynb](notebooks/6_scheduling_materialization_jobs.ipynb) | - |
-| 5 | Building personalized real-time recommenders with LLMs | Learn how to enhance recommendations with LLMs (WIP) | - | - |
-
-
 ## Install and usage
 
 To understand how to install and run the code, go to the [INSTALL_AND_USAGE](https://github.com/decodingml/hands-on-personalized-recommender/blob/main/INSTALL_AND_USAGE.md) 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.
 
+## Sponsors
+
+
+
 ## 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
-
+## Questions and troubleshooting

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "langchain-openai==0.1.14",
     "langchain==0.2.6",
     "loguru>=0.7.2",
+    "nbformat>=5.10.4",
     "polars==1.9.0",
     "pydantic-settings>=2.6.1",
     "sentence-transformers==2.2.2",