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fix: changes following issue 4593
Signed-off-by: Theodor Mihalache <tmihalac@redhat.com>
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‎docs/getting-started/concepts/README.md‎

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[overview.md](overview.md)
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{% endcontent-ref %}
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{% content-ref url="project.md" %}
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[project.md](project.md)
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{% endcontent-ref %}
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{% content-ref url="data-ingestion.md" %}
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[data-ingestion.md](data-ingestion.md)
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{% endcontent-ref %}

‎docs/getting-started/concepts/feature-view.md‎

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* zero or more [entities](entity.md)
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* If the features are not related to a specific object, the feature view might not have entities; see [feature views without entities](feature-view.md#feature-views-without-entities) below.
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* a name to uniquely identify this feature view in the project.
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* (optional, but recommended) a schema specifying one or more [features](feature-view.md#feature) (without this, Feast will infer the schema by reading from the data source)
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* (optional, but recommended) a schema specifying one or more [features](feature-view.md#field) (without this, Feast will infer the schema by reading from the data source)
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* (optional, but recommended) metadata (for example, description, or other free-form metadata via `tags`)
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* (optional) a TTL, which limits how far back Feast will look when generating historical datasets
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‎docs/getting-started/concepts/overview.md‎

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### Feast project structure
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The top-level namespace within Feast is a **project**. Users define one or more [feature views](feature-view.md) within a project. Each feature view contains one or more [features](feature-view.md#feature). These features typically relate to one or more [entities](entity.md). A feature view must always have a [data source](data-ingestion.md), which in turn is used during the generation of training [datasets](feature-retrieval.md#dataset) and when materializing feature values into the online store.
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![](<../../.gitbook/assets/image (7).png>)
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**Projects** provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (`dev`, `staging`, `prod`).
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The top-level namespace within Feast is a [project](project.md).
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### Data ingestion
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‎docs/getting-started/concepts/project.md‎

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# Project
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Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (`dev`, `staging`, `prod`).
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![](<../../.gitbook/assets/image (7).png>)
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Users define one or more [feature views](feature-view.md) within a project. Each feature view contains one or more [features](feature-view.md#field). These features typically relate to one or more [entities](entity.md). A feature view must always have a [data source](data-ingestion.md), which in turn is used during the generation of training [datasets](feature-retrieval.md#dataset) and when materializing feature values into the online store.
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The concept of project provide the following benefits:
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**Logical Grouping**: Projects group related features together, making it easier to manage and track them.
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**Feature Definitions**: Within a project, you can define features, including their metadata, types, and sources. This helps standardize how features are created and consumed.
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**Isolation**: Projects provide a way to isolate different environments, such as development, testing, and production, ensuring that changes in one project do not affect others.
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**Collaboration**: By organizing features within projects, teams can collaborate more effectively, with clear boundaries around the features they are responsible for.
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**Access Control**: Projects can implement permissions, allowing different users or teams to access only the features relevant to their work.

‎docs/getting-started/quickstart.md‎

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# Quickstart
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In this tutorial we will
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## What is Feast?
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Feast (Feature Store) is an open-source feature store designed to facilitate the management and serving of machine learning features in a way that supports both batch and real-time applications.
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For more info refer to [Introduction to feast](../README.md)
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## Prerequisites
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* Ensure that you have Python (3.9 or above) installed.
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* It is recommended to create and work in a virtual environment:
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```sh
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# create & activate a virtual environment
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python -m venv venv/
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source venv/bin/activate
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```
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## Overview
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In this tutorial we will:
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1. Deploy a local feature store with a **Parquet file offline store** and **Sqlite online store**.
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2. Build a training dataset using our time series features from our **Parquet files**.
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5. Read the latest features from the online store for real-time inference.
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6. Explore the (experimental) Feast UI
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## Overview
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***Note*** - Feast can used as an executable or as a server, please refer to [feast feature server](../reference/feature-servers/python-feature-server.md)
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In this tutorial, we'll use Feast to generate training data and power online model inference for a
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ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:
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7. Verify online features are updated / fresher
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We'll walk through some snippets of code below and explain
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### Step 3a: Register feature definitions and deploy your feature store
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### Step 4: Register feature definitions and deploy your feature store
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The `apply` command scans python files in the current directory for feature view/entity definitions, registers the
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objects, and deploys infrastructure. In this example, it reads `example_repo.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by
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{% endtab %}
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{% endtabs %}
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### Step 3b: Generating training data or powering batch scoring models
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### Step 5: Generating training data or powering batch scoring models
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To train a model, we need features and labels. Often, this label data is stored separately (e.g. you have one table storing user survey results and another set of tables with feature values). Feast can help generate the features that map to these labels.
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```
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{% endtab %}
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### Step 3c: Ingest batch features into your online store
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### Step 6: Ingest batch features into your online store
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`materialize-incremental` serializes all new features since the last `materialize` call).
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{% endtab %}
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{% endtabs %}
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### Step 3d: Fetching feature vectors for inference
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### Step 7: Fetching feature vectors for inference
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have existed only in batch sources) from the online feature store using `get_online_features()`. These feature
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### Step 3e: Using a feature service to fetch online features instead.
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### Step 8: Using a feature service to fetch online features instead.
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## Step 4: Browse your features with the Web UI (experimental)
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## Step 9: Browse your features with the Web UI (experimental)
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View all registered features, data sources, entities, and feature services with the Web UI.
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## Step 5: Re-examine `test_workflow.py`
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## Step 10: Re-examine `test_workflow.py`
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Take a look at `test_workflow.py` again. It showcases many sample flows on how to interact with Feast. You'll see these
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‎sdk/python/feast/templates/athena/.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*.pyo
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*.pyd
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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*.egg-info/
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dist/
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build/
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# Pytest
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.cache
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*.cover
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*.log
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.coverage
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nosetests.xml
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coverage.xml
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*.hypothesis/
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*.pytest_cache/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IDEs and Editors
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.vscode/
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.idea/
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*.swp
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*.swo
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*.sublime-workspace
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*.sublime-project
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.DS_Store
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‎sdk/python/feast/templates/aws/.gitignore‎

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*.py[cod]
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*.pyo
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*.pyd
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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*.egg-info/
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dist/
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build/
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# Pytest
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.cache
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*.cover
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*.log
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.coverage
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nosetests.xml
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coverage.xml
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*.hypothesis/
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*.pytest_cache/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IDEs and Editors
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.vscode/
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.idea/
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*.swp
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*.swo
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*.sublime-workspace
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*.sublime-project
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# OS generated files
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‎sdk/python/feast/templates/cassandra/.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*.pyo
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*.pyd
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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*.egg-info/
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dist/
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build/
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# Pytest
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.cache
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*.cover
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*.log
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.coverage
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nosetests.xml
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coverage.xml
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*.hypothesis/
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*.pytest_cache/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IDEs and Editors
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.vscode/
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.idea/
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*.swp
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*.swo
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*.sublime-workspace
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*.sublime-project
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‎sdk/python/feast/templates/cassandra/bootstrap.py‎

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‎sdk/python/feast/templates/gcp/.gitignore‎

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__pycache__/
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*.py[cod]
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*.pyo
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*.pyd
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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*.egg-info/
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dist/
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build/
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# Pytest
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.cache
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*.cover
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*.log
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.coverage
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nosetests.xml
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coverage.xml
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*.hypothesis/
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*.pytest_cache/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IDEs and Editors
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.vscode/
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.idea/
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*.swp
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*.swo
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*.sublime-workspace
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*.sublime-project
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# OS generated files
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.DS_Store
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Thumbs.db

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