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@@ -27,23 +27,23 @@ This is a quick-start of the Hopsworks Feature Store; using a fraud use case we
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This is a batch use case variant of the fraud tutorial, it will give you a high level view on how to use our python APIs and the UI to navigate the feature groups.
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| Notebooks |
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| -----------|
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| 1. [How to load, engineer and create feature groups](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/fraud_batch/1_fraud_batch_feature_pipeline.ipynb){:target="_blank"} |
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| Notebooks |
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| --- |
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| 1. [How to load, engineer and create feature groups](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/fraud_batch/1_fraud_batch_feature_pipeline.ipynb){:target="_blank"} |
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| 2. [How to create training datasets](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/fraud_batch/2_fraud_batch_training_pipeline.ipynb){:target="_blank"} |
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| 3. [How to train a model from the feature store](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/fraud_batch/3_fraud_batch_inference.ipynb){:target="_blank"} |
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| 3. [How to train a model from the feature store](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/fraud_batch/3_fraud_batch_inference.ipynb){:target="_blank"} |
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### Online
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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
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at low latency.
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Additionally, the model will be deployed as a model serving instance, to provide a REST endpoint for real time serving.
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| Notebooks |
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| -----------|
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| 1. [How to load, engineer and create feature groups](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/real-time-ai-systems/fraud_online/1_fraud_online_feature_pipeline.ipynb){:target="_blank"} |
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| Notebooks |
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| --- |
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| 1. [How to load, engineer and create feature groups](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/real-time-ai-systems/fraud_online/1_fraud_online_feature_pipeline.ipynb){:target="_blank"} |
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| 2. [How to create training datasets](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/real-time-ai-systems/fraud_online/2_fraud_online_training_pipeline.ipynb){:target="_blank"} |
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| 3. [How to train a model from the feature store and deploying it as a serving instance together with the online feature store](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/real-time-ai-systems/fraud_online/3_fraud_online_inference_pipeline.ipynb){:target="_blank"} |
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| 3. [How to train a model from the feature store and deploying it as a serving instance together with the online feature store](https://github.com/logicalclocks/hopsworks-tutorials/blob/master/real-time-ai-systems/fraud_online/3_fraud_online_inference_pipeline.ipynb){:target="_blank"} |
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## Churn Tutorial
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at low latency.
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Additionally, the model will be deployed as a model serving instance, to provide a REST endpoint for real time serving.
| 1. How to load, engineer and create feature groups |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/1_churn_feature_pipeline.ipynb){:target="_blank"} |
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| 2. How to create training datasets |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/2_churn_training_pipeline.ipynb){:target="_blank"} |
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| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/3_churn_batch_inference.ipynb){:target="_blank"} |
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| Notebooks ||
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| --- | --- |
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| 1. How to load, engineer and create feature groups |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/1_churn_feature_pipeline.ipynb){:target="_blank"} |
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| 2. How to create training datasets |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/2_churn_training_pipeline.ipynb){:target="_blank"} |
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| 3. How to train a model from the feature store and deploying it as a serving instance together with the online feature store |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/batch-ai-systems/churn/3_churn_batch_inference.ipynb){:target="_blank"} |
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## Integration Tutorials
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Great Expectations is a library for data validation.
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You can use Great Expectations within Hopsworks to validate data which is to be inserted into the feature store, in order to ensure that only high-quality features end up in the feature store.
| 1. A brief introduction to Great Expectations concepts which are relevant for integration with the Hopsworks MLOps platform |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/great_expectations/Great_Expectations_Hopsworks_Concepts.ipynb){:target="_blank"} |
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| 2. How to integrate Great Expectations seamlessly with your Hopsworks feature pipelines |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/great_expectations/fraud_batch_data_validation.ipynb){:target="_blank"} |
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This tutorial is a variant of the batch fraud tutorial using Weights and Biases for model training, tracking and as model registry.
| 1. How to load, engineer and create feature groups |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/1_feature_groups.ipynb){:target="_blank"} |
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| 2. How to create training datasets |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/2_feature_view_creation.ipynb){:target="_blank"} |
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| 3. How to train a model from the feature store and use Weights and Biases to track the process |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/3_model_training.ipynb){:target="_blank"} |
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| Notebooks ||
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| --- | --- |
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| 1. How to load, engineer and create feature groups |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/1_feature_groups.ipynb){:target="_blank"} |
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| 2. How to create training datasets |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/2_feature_view_creation.ipynb){:target="_blank"} |
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| 3. How to train a model from the feature store and use Weights and Biases to track the process |[](https://colab.research.google.com/github/logicalclocks/hopsworks-tutorials/blob/master/integrations/wandb/3_model_training.ipynb){:target="_blank"} |
The Hopsworks library has several profiles that bring additional dependencies and enable additional functionalities:
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| Profile Name | Description|
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| No Profile | This is the base installation. Supports interacting with the feature store metadata, model registry and deployments. It also supports reading and writing from the feature store from PySpark environments. |
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|`python`| This profile enables reading and writing from/to the feature store from a Python environment |
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| Profile Name | Description |
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| --- | --- |
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| No Profile | This is the base installation. Supports interacting with the feature store metadata, model registry and deployments. It also supports reading and writing from the feature store from PySpark environments. |
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|`python`| This profile enables reading and writing from/to the feature store from a Python environment |
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|`great-expectations`| This profile installs the [Great Expectations](https://greatexpectations.io/) Python library and enables data validation on feature pipelines |
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|`polars`| This profile installs the [Polars](https://pola.rs/) library and enables reading and writing Polars DataFrames |
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|`polars`| This profile installs the [Polars](https://pola.rs/) library and enables reading and writing Polars DataFrames |
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You can install all the above profiles with the following command:
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