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Update Colab links in docs for 3.2
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Maxxx-zh committed Sep 26, 2023
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Expand Up @@ -24,19 +24,19 @@ This is a batch use case variant of the fraud tutorial, it will give you a high

| Notebooks | |
| ----------- | ------------------------------------ |
| 1. How to load, engineer and create feature groups | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/1_feature_groups.ipynb){:target="_blank"} |
| 2. How to create training datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/2_feature_view_creation.ipynb){:target="_blank"} |
| 3. How to train a model from the feature store | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/3_model_training.ipynb){:target="_blank"} |
| 1. How to load, engineer and create feature groups | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/1_fraud_batch_feature_pipeline.ipynb){:target="_blank"} |
| 2. How to create training datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/2_fraud_batch_training_pipeline.ipynb){:target="_blank"} |
| 3. How to train a model from the feature store | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_batch/3_fraud_batch_inference.ipynb){:target="_blank"} |

### Online
This is a online use case variant of the fraud tutorial, it is similar to the batch use case, however, in this tutorial you will get introduced to the usage of Feature Groups which are kept in online storage, and how to access single feature vectors from the online storage
at low latency. Additionally, the model will be deployed as a model serving instance, to provide a REST endpoint for real time serving.

| Notebooks | |
| ----------- | ------------------------------------ |
| 1. How to load, engineer and create feature groups | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/1_feature_groups.ipynb){:target="_blank"} |
| 2. How to create training datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/2_feature_view_creation.ipynb){:target="_blank"} |
| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/3_model_training.ipynb){:target="_blank"} |
| 1. How to load, engineer and create feature groups | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/1_fraud_online_feature_pipeline.ipynb){:target="_blank"} |
| 2. How to create training datasets | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/2_fraud_online_training_pipeline.ipynb){:target="_blank"} |
| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/fraud_online/3_fraud_online_inference_pipeline.ipynb){:target="_blank"} |

## Churn Tutorial

Expand All @@ -45,17 +45,17 @@ at low latency. Additionally, the model will be deployed as a model serving inst

| Notebooks | |
| ----------- | ------------------------------------ |
| 1. How to load, engineer and create feature groups | [![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_feature_groups.ipynb){:target="_blank"} |
| 2. How to create training datasets | [![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_feature_view_creation.ipynb){:target="_blank"} |
| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store | [![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_model_training.ipynb){:target="_blank"} |
| 1. How to load, engineer and create feature groups | [![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){:target="_blank"} |
| 2. How to create training datasets | [![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){:target="_blank"} |
| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store | [![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){:target="_blank"} |

## Iris Tutorial

In this tutorial you will learn how to create an online prediction service for the Iris flower prediction problem.

| Notebooks | |
| ----------- | ------------------------------------ |
| 1. All-in-one notebook, showing how to create the needed feature groups, train the model and deploy it as a serving instance | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/iris/iris_sklearn.ipynb){:target="_blank"} |
| 1. All-in-one notebook, showing how to create the needed feature groups, train the model and deploy it as a serving instance | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/iris/iris_tutorial.ipynb){:target="_blank"} |

## Integration Tutorials

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