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<docs>: remove GitBook plugin from README #384

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39 changes: 16 additions & 23 deletions docs/concepts.md
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Expand Up @@ -20,31 +20,28 @@ Feature sets allow for groups of fields in these data sources to be ingested and

When data is loaded from these sources, each field in the feature set must be found in every record of the data source. Fields from these data sources must be either a timestamp, an entity, or a feature.

{% hint style="info" %}
Feature sets are a grouping of feature sets based on how they are loaded into Feast. They ensure that data is efficiently stored during ingestion. Feature sets are not a grouping of features for retrieval of features. During retrieval it is possible to retrieve feature values from any number of feature sets.
{% endhint %}

#### Customer Transactions Example

Below is an example of a basic `customer transactions` feature set that has been exported to YAML:

{% tabs %}
{% tab title="customer\_transactions\_feature\_set.yaml" %}
```yaml
name: customer_transactions
kind: feature_set
entities:
- name: customer_id
valueType: INT64
features:
- name: daily_transactions
valueType: FLOAT
- name: total_transactions
valueType: FLOAT
maxAge: 3600s
```
{% endtab %}
{% endtabs %}
* `customer\_transactions\_feature\_set.yaml`:

```yaml
name: customer_transactions
kind: feature_set
entities:
- name: customer_id
valueType: INT64
features:
- name: daily_transactions
valueType: FLOAT
- name: total_transactions
valueType: FLOAT
maxAge: 3600s
```


The dataframe below \(`customer_data.csv`\) contains the features and entities of the above feature set

Expand Down Expand Up @@ -74,9 +71,7 @@ A feature is an individual measurable property or characteristic of a phenomenon

In the context of Feast, features are values that are associated with either one or more entities over time. In Feast, these values are either primitives or lists of primitives. Each feature can also have additional information attached to it. For example whether it is a categorical feature or numerical.

{% hint style="info" %}
Features in Feast are defined within Feature Sets and are not treated as standalone concepts.
{% endhint %}

### Entity

Expand All @@ -91,9 +86,7 @@ An entity is the object on which features are observed. For example we could hav

In the context of Feast, entities are important because they are used as keys when looking up feature values. Entities are also used when joining feature values between different feature sets in order to build one large data set to train a model, or to serve a model.

{% hint style="info" %}
Entities in Feast are defined within Feature Sets and are not treated as standalone concepts.
{% endhint %}

### Types

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1 change: 0 additions & 1 deletion docs/getting-started/installing-feast.md
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Expand Up @@ -191,7 +191,6 @@ This guide will install Feast into a Kubernetes cluster on GCP. It assumes that
* Retrieve features for model training.
* Retrieve features for online serving.

{% hint style="info" %}
This guide requires [Google Cloud Platform](https://cloud.google.com/) for installation.

* [BigQuery](https://cloud.google.com/bigquery/) is used for storing historical features.
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