docs: Update docs

Author: iusztinpaul

.github/workflows/ml_pipelines.yaml

@@ -68,7 +68,7 @@ jobs:
       - uses: ./.github/actions/setup
 
       - name: Run pipeline
-        run: uv run ipython notebooks/4_fp_computing_item_embeddings.ipynb
+        run: uv run ipython notebooks/4_ip_computing_item_embeddings.ipynb
         env:
           HOPSWORKS_API_KEY: ${{ secrets.HOPSWORKS_API_KEY }}
 

INSTALL_AND_USAGE.md

@@ -81,7 +81,7 @@ You can also run each component separately:
    ```
 
 4. **Create Embeddings**
-   Execute the embeddings computation Notebook (`notebooks/4_fp_computing_item_embeddings.ipynb`):
+   Execute the embeddings computation Notebook (`notebooks/4_ip_computing_item_embeddings.ipynb`):
    ```bash
    make create-embeddings
    ```

Makefile

@@ -22,7 +22,7 @@ train-ranking:
 	uv run ipython notebooks/3_tp_training_ranking_model.ipynb
 
 create-embeddings:
-	uv run ipython notebooks/4_fp_computing_item_embeddings.ipynb
+	uv run ipython notebooks/4_ip_computing_item_embeddings.ipynb
 
 create-deployments:
 	uv run ipython notebooks/5_ip_creating_deployments.ipynb

README.md

@@ -14,7 +14,7 @@
 
 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:
+📙 **You will learn:**
 
 - building a recommender using the 4-stage recommender architecture
 - training a two-tower model for generating users and item embeddings
@@ -57,7 +57,7 @@ At Decoding ML we teach how to build production ML systems, thus the course foll
 | 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), [6_scheduling_materialization_jobs.ipynb](notebooks/6_scheduling_materialization_jobs.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) | - | - |