Updating links in the 4.5 branch

README.md

@@ -24,59 +24,59 @@ Familiarity with Machine Learning and Python development is recommended. For mor
 
 ## 🗄️ Table of Content:
 
-- [QuickStart](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/quickstart.ipynb): Introductory tutorial to get started quickly.
+- [QuickStart](https://github.com/logicalclocks/hopsworks-tutorials/blob/branch-4.5/quickstart.ipynb): Introductory tutorial to get started quickly.
 
 ### 🚀 Real-time AI Systems
-- [Fraud Online](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/real-time-ai-systems/fraud_online): Detect Fraud Transactions
-- [AML](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/real-time-ai-systems/aml): Anti-money laundering predictions
-- [TikTok RecSys](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/real-time-ai-systems/tiktok_recsys): TikTok-style recommendation system
-- [TimeSeries](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/real-time-ai-systems/timeseries): Timeseries price prediction
+- [Fraud Online](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/real-time-ai-systems/fraud_online): Detect Fraud Transactions
+- [AML](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/real-time-ai-systems/aml): Anti-money laundering predictions
+- [TikTok RecSys](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/real-time-ai-systems/tiktok_recsys): TikTok-style recommendation system
+- [TimeSeries](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/real-time-ai-systems/timeseries): Timeseries price prediction
 
 ### ⚙️ Batch AI Systems
-- [Loan Approval](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/loan_approval): Predict loan approvals
-- [Fraud Batch](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/fraud_batch): Detect Fraud Transactions
-- [Churn](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/churn): Predict customers at risk of churning
-- [Credit Scores](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/credit_scores): Predict clients' repayment abilities
-- [Hospital Wait Time](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/hospital_wait_time): Predict waiting time for deceased donor kidneys
-- [NYC Taxi Fares](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/batch-ai-systems/nyc_taxi_fares): Predict NYC taxi fare amounts
+- [Loan Approval](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/loan_approval): Predict loan approvals
+- [Fraud Batch](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/fraud_batch): Detect Fraud Transactions
+- [Churn](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/churn): Predict customers at risk of churning
+- [Credit Scores](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/credit_scores): Predict clients' repayment abilities
+- [Hospital Wait Time](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/hospital_wait_time): Predict waiting time for deceased donor kidneys
+- [NYC Taxi Fares](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/batch-ai-systems/nyc_taxi_fares): Predict NYC taxi fare amounts
 
 ### 🔮 LLM AI Systems
-- [Fraud Cheque Detection](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/llm-ai-systems/fraud_cheque_detection): AI assistant for detecting fraudulent scanned cheques
-- [LLM PDF](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/llm-ai-systems/llm_pdfs): RAG-based AI assistant for PDF document Q&A
-- [Recommender System](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/llm-ai-systems/recommender-system): Fashion items recommender system
+- [Fraud Cheque Detection](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/llm-ai-systems/fraud_cheque_detection): AI assistant for detecting fraudulent scanned cheques
+- [LLM PDF](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/llm-ai-systems/llm_pdfs): RAG-based AI assistant for PDF document Q&A
+- [Recommender System](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/llm-ai-systems/recommender-system): Fashion items recommender system
 
 ### 🧬 API Examples
 - Vector Similarity Search:
-  - [Feature Group Embeddings API](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/vector_similarity_search/1_feature_group_embeddings_api.ipynb)
-  - [Feature View Embeddings API](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/vector_similarity_search/2_feature_view_embeddings_api.ipynb)
-- [Datasets](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/datasets.ipynb)
-- [Feature Group Change Notification CDC](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/feature_group_change_notification_cdc.ipynb)
-- [Feature Monitoring](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/feature_monitoring.ipynb)
-- [Git Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/git.ipynb)
-- [Jobs Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/jobs.ipynb)
-- [Kafka Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/kafka.ipynb)
-- [OpenSearch Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/opensearch.ipynb)
-- [Projects Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/projects.ipynb)
-- [Secrets Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/secrets.ipynb)
+  - [Feature Group Embeddings API](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/vector_similarity_search/1_feature_group_embeddings_api.ipynb)
+  - [Feature View Embeddings API](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/vector_similarity_search/2_feature_view_embeddings_api.ipynb)
+- [Datasets](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/datasets.ipynb)
+- [Feature Group Change Notification CDC](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/feature_group_change_notification_cdc.ipynb)
+- [Feature Monitoring](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/feature_monitoring.ipynb)
+- [Git Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/git.ipynb)
+- [Jobs Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/jobs.ipynb)
+- [Kafka Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/kafka.ipynb)
+- [OpenSearch Integration](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/opensearch.ipynb)
+- [Projects Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/projects.ipynb)
+- [Secrets Management](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/secrets.ipynb)
 
 ### 🔬 Integrations
-- [Airflow GCP](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/airflow_gcp): Apache Airflow integration with Google Cloud Platform.
-- [AzureSQL](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/azuresql): Create an External Feature Group using Azure SQL Database.
-- [BigQuery](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/big_query): Create an External Feature Group using BigQuery Storage Connector.
-- [Bytewax](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/bytewax): Real-time feature computation using Bytewax.
-- [DBT with BigQuery](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/dbt_bq): Perform feature engineering in DBT on BigQuery.
-- [Federated Offline Query](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/federated-offline-query): Execute federated queries across offline data sources.
-- [Google Cloud Storage](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/gcs): Create an External Feature Group using GCS Storage Connector.
-- [Great Expectations](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/great_expectations): Introduction to Great Expectations concepts for Hopsworks MLOps platform.
-- [Java](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/java): Java-based integrations including Apache Beam and Apache Flink.
-- [LangChain](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/langchain): Integration with LangChain for LLM applications.
-- [MageAI](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/mage_ai): Build and operate ML systems with Mage and Hopsworks.
-- [Neo4j](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/neo4j): Perform Anti-money laundering predictions using Neo4j Graph.
-- [Polars](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/polars): Introductory tutorial on using Polars with Hopsworks.
-- [PySpark Streaming](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/pyspark_streaming): Real-time feature computation using PySpark.
-- [Redshift](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/redshift): Create an External Feature Group using Redshift Storage Connector.
-- [Snowflake](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/snowflake): Create an External Feature Group using Snowflake Storage Connector.
-- [Weights & Biases](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/integrations/wandb): Build machine learning models with Weights & Biases.
+- [Airflow GCP](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/airflow_gcp): Apache Airflow integration with Google Cloud Platform.
+- [AzureSQL](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/azuresql): Create an External Feature Group using Azure SQL Database.
+- [BigQuery](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/big_query): Create an External Feature Group using BigQuery Storage Connector.
+- [Bytewax](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/bytewax): Real-time feature computation using Bytewax.
+- [DBT with BigQuery](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/dbt_bq): Perform feature engineering in DBT on BigQuery.
+- [Federated Offline Query](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/federated-offline-query): Execute federated queries across offline data sources.
+- [Google Cloud Storage](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/gcs): Create an External Feature Group using GCS Storage Connector.
+- [Great Expectations](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/great_expectations): Introduction to Great Expectations concepts for Hopsworks MLOps platform.
+- [Java](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/java): Java-based integrations including Apache Beam and Apache Flink.
+- [LangChain](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/langchain): Integration with LangChain for LLM applications.
+- [MageAI](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/mage_ai): Build and operate ML systems with Mage and Hopsworks.
+- [Neo4j](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/neo4j): Perform Anti-money laundering predictions using Neo4j Graph.
+- [Polars](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/polars): Introductory tutorial on using Polars with Hopsworks.
+- [PySpark Streaming](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/pyspark_streaming): Real-time feature computation using PySpark.
+- [Redshift](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/redshift): Create an External Feature Group using Redshift Storage Connector.
+- [Snowflake](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/snowflake): Create an External Feature Group using Snowflake Storage Connector.
+- [Weights & Biases](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/integrations/wandb): Build machine learning models with Weights & Biases.
 
 ## 📝 Feedback & Comments:
 We welcome your input through:

api_examples/vector_similarity_search/1_feature_group_embeddings_api.ipynb

@@ -324,7 +324,7 @@
     "\n",
     "## <span style=\"color:#ff5f27;\">➡️ Next step</span>\n",
     "\n",
-    "Now you are able to search articles using natural language. You can learn how to rank the result in [this tutorial](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/vector_similarity_search/2_feature_view_embeddings_api.ipynb)."
+    "Now you are able to search articles using natural language. You can learn how to rank the result in [this tutorial](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/vector_similarity_search/2_feature_view_embeddings_api.ipynb)."
    ]
   }
  ],

api_examples/vector_similarity_search/2_feature_view_embeddings_api.ipynb

@@ -13,7 +13,7 @@
    "id": "8988ff65",
    "metadata": {},
    "source": [
-    "In the [previous tutorial](https://github.com/logicalclocks/hopsworks-tutorials/tree/master/api_examples/vector_similarity_search/1_feature_group_embeddings_api.ipynb), you learned how to search news articles using natural language queries. In this tutorial, we will focus on ranking the search results to make them more useful and relevant.\n",
+    "In the [previous tutorial](https://github.com/logicalclocks/hopsworks-tutorials/tree/branch-4.5/api_examples/vector_similarity_search/1_feature_group_embeddings_api.ipynb), you learned how to search news articles using natural language queries. In this tutorial, we will focus on ranking the search results to make them more useful and relevant.\n",
     "\n",
     "To achieve this, we will use the number of views as a scoring metric for news articles, as it reflects their popularity. The steps are as follows:\n",
     "\n",

batch-ai-systems/churn/1_churn_feature_pipeline.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# <span style=\"font-width:bold; font-size: 3rem; color:#1EB182;\"><img src=\"../images/icon102.png\" width=\"38px\"></img> **Hopsworks Feature Store** </span><span style=\"font-width:bold; font-size: 3rem; color:#333;\">- Part 01: Feature Pipeline</span>\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/churn/1_churn_feature_pipeline.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/branch-4.5/churn/1_churn_feature_pipeline.ipynb)\n",
     "\n",
     "\n",
     "## 🗒️ This notebook is divided into the following sections:\n",
@@ -367,7 +367,7 @@
     "\n",
     "In the following notebook you will use your feature groups to create a train dataset, train a model and add a trained model to model registry.\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/churn/2_churn_training_pipeline.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/branch-4.5/churn/2_churn_training_pipeline.ipynb)"
    ]
   }
  ],

batch-ai-systems/churn/2_churn_training_pipeline.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# <span style=\"font-width:bold; font-size: 3rem; color:#1EB182;\"><img src=\"../images/icon102.png\" width=\"38px\"></img> **Hopsworks Feature Store** </span><span style=\"font-width:bold; font-size: 3rem; color:#333;\">- Part 02: Training Pipeline</span>\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/churn/2_churn_training_pipeline.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/branch-4.5/churn/2_churn_training_pipeline.ipynb)\n",
     "\n",
     "This is the second part of the quick start series of tutorials about predicting customers that are at risk of churning with the Hopsworks Feature Store.\n",
     "\n",
@@ -455,7 +455,7 @@
     "\n",
     "In the following notebook you will use your model for batch inference.\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/churn/3_churn_batch_inference.ipynb)\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/branch-4.5/churn/3_churn_batch_inference.ipynb)\n",
     "\n",
     "---"
    ]

batch-ai-systems/churn/3_churn_batch_inference.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# <span style=\"font-width:bold; font-size: 3rem; color:#1EB182;\"><img src=\"../images/icon102.png\" width=\"38px\"></img> **Hopsworks Feature Store** </span><span style=\"font-width:bold; font-size: 3rem; color:#333;\">- Part 03: Batch Inference</span>\n",
     "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/churn/3_churn_batch_inference.ipynb)"
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/branch-4.5/churn/3_churn_batch_inference.ipynb)"
    ]
   },
   {
@@ -451,7 +451,7 @@
     "> `conda activate ./miniconda/envs/hopsworks` </br>\n",
     "> `python -m streamlit run churn/streamlit_app.py`</br>\n",
     "\n",
-    "**⚠️** If you are running on Colab, you will need to follow a different procedure. As highlighted in this [notebook](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Create_streamlit_app.ipynb). "
+    "**⚠️** If you are running on Colab, you will need to follow a different procedure. As highlighted in this [notebook](https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/branch-4.5/Create_streamlit_app.ipynb). "
    ]
   },
   {

batch-ai-systems/nyc_taxi_fares/README.md

@@ -30,7 +30,7 @@ Also, you obviously need to have [streamlit](https://docs.streamlit.io/library/g
 
 
 ## Data
-You will generate random data for this tutorial. See corresponding functions in the [functions.py](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/advanced_tutorials/nyc_taxi_fares/functions.py).
+You will generate random data for this tutorial. See corresponding functions in the [functions.py](https://github.com/logicalclocks/hopsworks-tutorials/blob/branch-4.5/advanced_tutorials/nyc_taxi_fares/functions.py).
 
 
 ## Streamlit run