Quickstart

What is Feast?

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.

For Data Scientists: Feast is a a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
For MLOps Engineers: Feast is a library that allows you to connect your existing infrastructure (e.g., online database, application server, microservice, analytical database, and orchestration tooling) that enables your Data Scientists to ship features for their models to production using a friendly SDK without having to be concerned with software engineering challenges that occur from serving real-time production systems. By using Feast, you can focus on maintaining a resilient system, instead of implementing features for Data Scientists.
For Data Engineers: Feast provides a centralized catalog for storing feature definitions allowing one to maintain a single source of truth for feature data. It provides the abstraction for reading and writing to many different types of offline and online data stores. Using either the provided python SDK or the feature server service, users can write data to the online and/or offline stores and then read that data out again in either low-latency online scenarios for model inference, or in batch scenarios for model training.
For AI Engineers: Feast provides a platform designed to scale your AI applications by enabling seamless integration of richer data and facilitating fine-tuning. With Feast, you can optimize the performance of your AI models while ensuring a scalable and efficient data pipeline.

For more info refer to Introduction to feast

Prerequisites

Ensure that you have Python (3.9 or above) installed.

It is recommended to create and work in a virtual environment:

# create & activate a virtual environment
python -m venv venv/
source venv/bin/activate

Overview

In this tutorial we will:

Deploy a local feature store with a Parquet file offline store and Sqlite online store.
Build a training dataset using our time series features from our Parquet files.
Ingest batch features ("materialization") and streaming features (via a Push API) into the online store.
Read the latest features from the offline store for batch scoring
Read the latest features from the online store for real-time inference.
Explore the (experimental) Feast UI

Note - Feast provides a python SDK as well as an optional hosted service for reading and writing feature data to the online and offline data stores. The latter might be useful when non-python languages are required.

For this tutorial, we will be using the python SDK.

In this tutorial, we'll use Feast to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:

Training-serving skew and complex data joins: Feature values often exist across multiple tables. Joining these datasets can be complicated, slow, and error-prone.
- Feast joins these tables with battle-tested logic that ensures point-in-time correctness so future feature values do not leak to models.
Online feature availability: At inference time, models often need access to features that aren't readily available and need to be precomputed from other data sources.
- Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently available and freshly computed at inference time.
Feature and model versioning: Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.
- Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via feature services).
- (Experimental) Feast enables light-weight feature transformations so users can re-use transformation logic across online / offline use cases and across models.

Step 1: Install Feast

Install the Feast SDK and CLI using pip:

In this tutorial, we focus on a local deployment. For a more in-depth guide on how to use Feast with Snowflake / GCP / AWS deployments, see Running Feast with Snowflake/GCP/AWS

pip install feast

Step 2: Create a feature repository

Bootstrap a new feature repository using feast init from the command line.

feast init my_project
cd my_project/feature_repo

Creating a new Feast repository in /home/Jovyan/my_project.

Let's take a look at the resulting demo repo itself. It breaks down into

data/ contains raw demo parquet data
feature_definitions.py contains demo feature definitions
feature_store.yaml contains a demo setup configuring where data sources are
test_workflow.py showcases how to run all key Feast commands, including defining, retrieving, and pushing features. You can run this with python test_workflow.py.

project: my_project
# By default, the registry is a file (but can be turned into a more scalable SQL-backed registry)
registry: data/registry.db
# The provider primarily specifies default offline / online stores & storing the registry in a given cloud
provider: local
online_store:
  type: sqlite
  path: data/online_store.db
entity_key_serialization_version: 3

# This is an example feature definition file

from datetime import timedelta

import pandas as pd

from feast import (
    Entity,
    FeatureService,
    FeatureView,
    Field,
    FileSource,
    Project,
    PushSource,
    RequestSource,
)
from feast.on_demand_feature_view import on_demand_feature_view
from feast.types import Float32, Float64, Int64

# Define a project for the feature repo
project = Project(name="my_project", description="A project for driver statistics")


# Define an entity for the driver. You can think of an entity as a primary key used to
# fetch features.
driver = Entity(name="driver", join_keys=["driver_id"])

# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_stats_source = FileSource(
    name="driver_hourly_stats_source",
    path="%PARQUET_PATH%",
    timestamp_field="event_timestamp",
    created_timestamp_column="created",
)

# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_stats_fv = FeatureView(
    # The unique name of this feature view. Two feature views in a single
    # project cannot have the same name
    name="driver_hourly_stats",
    entities=[driver],
    ttl=timedelta(days=1),
    # The list of features defined below act as a schema to both define features
    # for both materialization of features into a store, and are used as references
    # during retrieval for building a training dataset or serving features
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64, description="Average daily trips"),
    ],
    online=True,
    source=driver_stats_source,
    # Tags are user defined key/value pairs that are attached to each
    # feature view
    tags={"team": "driver_performance"},
)

# Define a request data source which encodes features / information only
# available at request time (e.g. part of the user initiated HTTP request)
input_request = RequestSource(
    name="vals_to_add",
    schema=[
        Field(name="val_to_add", dtype=Int64),
        Field(name="val_to_add_2", dtype=Int64),
    ],
)


# Define an on demand feature view which can generate new features based on
# existing feature views and RequestSource features
@on_demand_feature_view(
    sources=[driver_stats_fv, input_request],
    schema=[
        Field(name="conv_rate_plus_val1", dtype=Float64),
        Field(name="conv_rate_plus_val2", dtype=Float64),
    ],
)
def transformed_conv_rate(inputs: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df["conv_rate_plus_val1"] = inputs["conv_rate"] + inputs["val_to_add"]
    df["conv_rate_plus_val2"] = inputs["conv_rate"] + inputs["val_to_add_2"]
    return df


# This groups features into a model version
driver_activity_v1 = FeatureService(
    name="driver_activity_v1",
    features=[
        driver_stats_fv[["conv_rate"]],  # Sub-selects a feature from a feature view
        transformed_conv_rate,  # Selects all features from the feature view
    ],
)
driver_activity_v2 = FeatureService(
    name="driver_activity_v2", features=[driver_stats_fv, transformed_conv_rate]
)

# Defines a way to push data (to be available offline, online or both) into Feast.
driver_stats_push_source = PushSource(
    name="driver_stats_push_source",
    batch_source=driver_stats_source,
)

# Defines a slightly modified version of the feature view from above, where the source
# has been changed to the push source. This allows fresh features to be directly pushed
# to the online store for this feature view.
driver_stats_fresh_fv = FeatureView(
    name="driver_hourly_stats_fresh",
    entities=[driver],
    ttl=timedelta(days=1),
    schema=[
        Field(name="conv_rate", dtype=Float32),
        Field(name="acc_rate", dtype=Float32),
        Field(name="avg_daily_trips", dtype=Int64),
    ],
    online=True,
    source=driver_stats_push_source,  # Changed from above
    tags={"team": "driver_performance"},
)


# Define an on demand feature view which can generate new features based on
# existing feature views and RequestSource features
@on_demand_feature_view(
    sources=[driver_stats_fresh_fv, input_request],  # relies on fresh version of FV
    schema=[
        Field(name="conv_rate_plus_val1", dtype=Float64),
        Field(name="conv_rate_plus_val2", dtype=Float64),
    ],
)
def transformed_conv_rate_fresh(inputs: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame()
    df["conv_rate_plus_val1"] = inputs["conv_rate"] + inputs["val_to_add"]
    df["conv_rate_plus_val2"] = inputs["conv_rate"] + inputs["val_to_add_2"]
    return df


driver_activity_v3 = FeatureService(
    name="driver_activity_v3",
    features=[driver_stats_fresh_fv, transformed_conv_rate_fresh],
)

The feature_store.yaml file configures the key overall architecture of the feature store.

The provider value sets default offline and online stores.

The offline store provides the compute layer to process historical data (for generating training data & feature values for serving).
The online store is a low latency store of the latest feature values (for powering real-time inference).

Valid values for provider in feature_store.yaml are:

local: use a SQL registry or local file registry. By default, use a file / Dask based offline store + SQLite online store
gcp: use a SQL registry or GCS file registry. By default, use BigQuery (offline store) + Google Cloud Datastore (online store)
aws: use a SQL registry or S3 file registry. By default, use Redshift (offline store) + DynamoDB (online store)

Note that there are many other offline / online stores Feast works with, including Spark, Azure, Hive, Trino, and PostgreSQL via community plugins. See Third party integrations for all supported data sources.

A custom setup can also be made by following Customizing Feast.

Inspecting the raw data

The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.

import pandas as pd
pd.read_parquet("data/driver_stats.parquet")

Step 3: Run sample workflow

There's an included test_workflow.py file which runs through a full sample workflow:

Register feature definitions through feast apply
Generate a training dataset (using get_historical_features)
Generate features for batch scoring (using get_historical_features)
Ingest batch features into an online store (using materialize_incremental)
Fetch online features to power real time inference (using get_online_features)
Ingest streaming features into offline / online stores (using push)
Verify online features are updated / fresher

We'll walk through some snippets of code below and explain

Step 4: Register feature definitions and deploy your feature store

The apply command scans python files in the current directory for feature view/entity definitions, registers the objects, and deploys infrastructure. In this example, it reads feature_definitions.py and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by configuring online_store in feature_store.yaml.

feast apply

Created entity driver
Created feature view driver_hourly_stats
Created feature view driver_hourly_stats_fresh
Created on demand feature view transformed_conv_rate
Created on demand feature view transformed_conv_rate_fresh
Created feature service driver_activity_v3
Created feature service driver_activity_v1
Created feature service driver_activity_v2

Created sqlite table my_project_driver_hourly_stats_fresh
Created sqlite table my_project_driver_hourly_stats

Step 5: Generating training data or powering batch scoring models

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.

Feast needs a list of entities (e.g. driver ids) and timestamps. Feast will intelligently join relevant tables to create the relevant feature vectors. There are two ways to generate this list:

The user can query that table of labels with timestamps and pass that into Feast as an entity dataframe for training data generation.
The user can also query that table with a SQL query which pulls entities. See the documentation on feature retrieval for details

Note that we include timestamps because we want the features for the same driver at various timestamps to be used in a model.

Generating training data

from datetime import datetime
import pandas as pd

from feast import FeatureStore

# Note: see https://docs.feast.dev/getting-started/concepts/feature-retrieval for 
# more details on how to retrieve for all entities in the offline store instead
entity_df = pd.DataFrame.from_dict(
    {
        # entity's join key -> entity values
        "driver_id": [1001, 1002, 1003],
        # "event_timestamp" (reserved key) -> timestamps
        "event_timestamp": [
            datetime(2021, 4, 12, 10, 59, 42),
            datetime(2021, 4, 12, 8, 12, 10),
            datetime(2021, 4, 12, 16, 40, 26),
        ],
        # (optional) label name -> label values. Feast does not process these
        "label_driver_reported_satisfaction": [1, 5, 3],
        # values we're using for an on-demand transformation
        "val_to_add": [1, 2, 3],
        "val_to_add_2": [10, 20, 30],
    }
)

store = FeatureStore(repo_path=".")

training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
        "transformed_conv_rate:conv_rate_plus_val1",
        "transformed_conv_rate:conv_rate_plus_val2",
    ],
).to_df()

print("----- Feature schema -----\n")
print(training_df.info())

print()
print("----- Example features -----\n")
print(training_df.head())

----- Feature schema -----

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 10 columns):
 #   Column                              Non-Null Count  Dtype              
---  ------                              --------------  -----              
 0   driver_id                           3 non-null      int64              
 1   event_timestamp                     3 non-null      datetime64[ns, UTC]
 2   label_driver_reported_satisfaction  3 non-null      int64              
 3   val_to_add                          3 non-null      int64              
 4   val_to_add_2                        3 non-null      int64              
 5   conv_rate                           3 non-null      float32            
 6   acc_rate                            3 non-null      float32            
 7   avg_daily_trips                     3 non-null      int32              
 8   conv_rate_plus_val1                 3 non-null      float64            
 9   conv_rate_plus_val2                 3 non-null      float64            
dtypes: datetime64[ns, UTC](1), float32(2), float64(2), int32(1), int64(4)
memory usage: 336.0 bytes
None

----- Example features -----

   driver_id           event_timestamp  label_driver_reported_satisfaction  \
0       1001 2021-04-12 10:59:42+00:00                                   1   
1       1002 2021-04-12 08:12:10+00:00                                   5   
2       1003 2021-04-12 16:40:26+00:00                                   3   

   val_to_add  val_to_add_2  conv_rate  acc_rate  avg_daily_trips  \
0           1            10   0.800648  0.265174              643   
1           2            20   0.644141  0.996602              765   
2           3            30   0.855432  0.546345              954   

   conv_rate_plus_val1  conv_rate_plus_val2  
0             1.800648            10.800648  
1             2.644141            20.644141  
2             3.855432            30.855432

Run offline inference (batch scoring)

To power a batch model, we primarily need to generate features with the get_historical_features call, but using the current timestamp

entity_df["event_timestamp"] = pd.to_datetime("now", utc=True)
training_df = store.get_historical_features(
    entity_df=entity_df,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
        "transformed_conv_rate:conv_rate_plus_val1",
        "transformed_conv_rate:conv_rate_plus_val2",
    ],
).to_df()

print("\n----- Example features -----\n")
print(training_df.head())

----- Example features -----

   driver_id                  event_timestamp  \
0       1001 2024-04-19 14:58:16.452895+00:00   
1       1002 2024-04-19 14:58:16.452895+00:00   
2       1003 2024-04-19 14:58:16.452895+00:00   

   label_driver_reported_satisfaction  val_to_add  val_to_add_2  conv_rate  \
0                                   1           1            10   0.535773   
1                                   5           2            20   0.171976   
2                                   3           3            30   0.275669   

   acc_rate  avg_daily_trips  conv_rate_plus_val1  conv_rate_plus_val2  
0  0.689705              428             1.535773            10.535773  
1  0.737113              369             2.171976            20.171976  
2  0.156630              116             3.275669            30.275669

Step 6: Ingest batch features into your online store

We now serialize the latest values of features since the beginning of time to prepare for serving. Note, materialize_incremental serializes all new features since the last materialize call, or since the time provided minus the ttl timedelta. In this case, this will be CURRENT_TIME - 1 day (ttl was set on the FeatureView instances in feature_repo/feature_repo/feature_definitions.py).

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")

feast materialize-incremental $CURRENT_TIME

Materializing 2 feature views to 2024-04-19 10:59:58-04:00 into the sqlite online store.

driver_hourly_stats from 2024-04-18 15:00:46-04:00 to 2024-04-19 10:59:58-04:00:
100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 370.32it/s]
driver_hourly_stats_fresh from 2024-04-18 15:00:46-04:00 to 2024-04-19 10:59:58-04:00:
100%|███████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 1046.64it/s]
Materializing 2 feature views to 2024-04-19 10:59:58-04:00 into the sqlite online store.

Step 7: Fetching feature vectors for inference

At inference time, we need to quickly read the latest feature values for different drivers (which otherwise might have existed only in batch sources) from the online feature store using get_online_features(). These feature vectors can then be fed to the model.

from pprint import pprint
from feast import FeatureStore

store = FeatureStore(repo_path=".")

feature_vector = store.get_online_features(
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips",
    ],
    entity_rows=[
        # {join_key: entity_value}
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()

pprint(feature_vector)

{
 'acc_rate': [0.25351759791374207, 0.8949751853942871],
 'avg_daily_trips': [712, 791],
 'conv_rate': [0.5038306713104248, 0.9839504361152649],
 'driver_id': [1004, 1005]
 }

Step 8: Using a feature service to fetch online features instead.

You can also use feature services to manage multiple features, and decouple feature view definitions and the features needed by end applications. The feature store can also be used to fetch either online or historical features using the same API below. More information can be found here.

The driver_activity_v1 feature service pulls all features from the driver_hourly_stats feature view:

from feast import FeatureService
driver_stats_fs = FeatureService(
    name="driver_activity_v1", features=[driver_stats_fv]
)

from pprint import pprint
from feast import FeatureStore
feature_store = FeatureStore('.')  # Initialize the feature store

feature_service = feature_store.get_feature_service("driver_activity_v1")
feature_vector = feature_store.get_online_features(
    features=feature_service,
    entity_rows=[
        # {join_key: entity_value}
        {"driver_id": 1004},
        {"driver_id": 1005},
    ],
).to_dict()
pprint(feature_vector)

{
 'acc_rate': [0.5732735991477966, 0.7828438878059387],
 'avg_daily_trips': [33, 984],
 'conv_rate': [0.15498852729797363, 0.6263588070869446],
 'driver_id': [1004, 1005]
}

Step 9: Browse your features with the Web UI (experimental)

View all registered features, data sources, entities, and feature services with the Web UI.

One of the ways to view this is with the feast ui command.

feast ui

INFO:     Started server process [66664]
08/17/2022 01:25:49 PM uvicorn.error INFO: Started server process [66664]
INFO:     Waiting for application startup.
08/17/2022 01:25:49 PM uvicorn.error INFO: Waiting for application startup.
INFO:     Application startup complete.
08/17/2022 01:25:49 PM uvicorn.error INFO: Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)
08/17/2022 01:25:49 PM uvicorn.error INFO: Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)

Step 10: Re-examine `test_workflow.py`

Take a look at test_workflow.py again. It showcases many sample flows on how to interact with Feast. You'll see these show up in the upcoming concepts + architecture + tutorial pages as well.

Next steps

Read the Concepts page to understand the Feast data model.
Read the Architecture page.
Check out our Tutorials section for more examples on how to use Feast.
Follow our Running Feast with Snowflake/GCP/AWS guide for a more in-depth tutorial on using Feast.

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Good afternoon

hashtagWhat is Feast?

hashtagPrerequisites

hashtagOverview

hashtagStep 1: Install Feast

hashtagStep 2: Create a feature repository

hashtagInspecting the raw data

hashtagStep 3: Run sample workflow

hashtagStep 4: Register feature definitions and deploy your feature store

hashtagStep 5: Generating training data or powering batch scoring models

hashtagGenerating training data

hashtagRun offline inference (batch scoring)

hashtagStep 6: Ingest batch features into your online store

hashtagStep 7: Fetching feature vectors for inference

hashtagStep 8: Using a feature service to fetch online features instead.

hashtagStep 9: Browse your features with the Web UI (experimental)

hashtagStep 10: Re-examine test_workflow.py

hashtagNext steps