Feast Feature Store - An In-depth Overview Experimentation and Application in Tabular data.pdf

Feast Feature Store
CONFIDENTIAL | © 2024 EPAM Systems, Inc.
An In-depth Overview
Experimentation and Application
in Tabular data
29th March 2024
Presenters:
• Loc Nguyen (MLE): loc_nguyen@epam.com
• Hong Ong (Lead Data SE): hong_ong@epam.com

Agenda
• Motivation: Why do we need feature stores?
• Feast: Why do we choose Feast as feature stores?
• How to: Store and Serve by using Feast?
• Demo scenarios:
o As a DS: Quickly experiment based on pre-calculate features.
o As a DE: Data pipeline integration.
o As a MLE: Serving online APIs inferences.
• Discussion:
o Pros and Cons
o Extend beyond tabular data: Image, Text
o Datasource VectorDB
2

Motivation: Why do we need feature stores?
3

• Tricky in getting
other project's features for our
own project.
• Data scientist want to spend
time optimizing models, but
end up spending too much time
on data preparation.
P R O D U C T I O N R E A D Y
M A N A G E D A T A C O N S I S T E N C Y
L A C K O F D I S C O V E R Y
4
• Serving features in production is
hard-gap between feature get
from training and feature get in
production.
• Duplication of works.
• Difficulties in manage data
consistency across projects.
What are the challenges with features?

Ideas
5
Avoid duplicate effort and re-use code/outputs as much as possible.
• Centralize code base.
• Centralize calculated features.
Share calculated features across-projects and production ready.
• Easy in features discovery.
• Easy in retrieving features.
• Easy synchronize training phase and production phase.

CONFIDENTIAL | © 2024 EPAM Systems, Inc. 6
Source: link

Why do we choose Feast as feature stores?
7

Github reputation
8
Star Rating: Centralize code base. has over 5.2k
stars, indicating substantial popularity in the
open-source community.
Frequency of Updates: The repository
is frequently updated, implying that the tool is
actively being developed, with bugs being fixed
and new features being added regularly.

Feast: Design goals
9
• Share features between teams and projects to reduce duplicated effort.
• Provide data consistency across projects.
• Decouple feature engineering from model development (DS would love this).
• Provide access feature in real-time.
• Support integrated with existing tools that ML practitioners are familiar with (e.g. Airflow, Dagster, MLFlow, K8s).

Feast overview
10
Source: link

How to: Store and Serve by using Feast?
11

What is a feature ?
12
Useid Avg Loan late
payment rate
Avg income Avg credit
score
datetime
3001 0 1800 500 22/10/01
3002 0.2 1500 380 22/12/01
3001 0 2500 500 23/12/01
3002 0.3 1800 500 23/11/01
Features related to a user
User is the entity type

How do we train a loan model?
13
usesid age Income creditscore
3001 30 2000 400
3002 28 3500 570
Loanid userid datetim
e
Loan
amount
Interate
rate
Loan
term
DTI
rate
Default
1001 3001 23/01 10000 1.5 36 0.34 0
1002 3002 24/02 20000 1.4 40 0.48 1
Useid Avg Loan late
payment rate
Avg
income
Avg credit
score
datetime
3001 0 1800 500 22/10/01
3002 0.2 1500 380 22/12/01
3001 0 2500 500 23/12/01
3002 0.3 1800 500 23/11/01
Loan
user
age
Loan
user
currnet
income
Loan
user
current
credit
score
Avg
Loan
late
payme
nt rate
Avg
income
Avg
credit
score
Loan
amount
Interate
rate
Loan
term
DTI
rate
Default
30 2000 400 0 1800 500 10000 1.5 36 0.34 0
28 3500 570 0.3 1800 500 20000 1.4 40 0.48 1
Loan information
User
precomputed
feature
User information
1. Join features to target
2. Train model

How do we predict a loan transaction ?
14
userid Loan
user
age
Loan
user
currnet
income
Loan
user
current
credit
score
Avg
Loan
late
payme
nt rate
Avg
income
Avg
credit
score
Loan
amount
Interate
rate
Loan
term
DTI
rate
30003 30 2000 400 0 1800 500 10000 1.5 36 0.34
3. Incoming request
4. Predict loan default
Loan default rate
28.9%
But where do these
features come from ?

What are the challenge with features
15
"I want to spend my time optimizing
models, but I end up spending too much
time on data preparation"
• Spends most time creating and managing data
pipelines
• Lack of discovery leads to duplication work
• Doesn't want to think about how to get
features into production
• Doesn't want to worry about data consistency
between training and serving

What are the challenge with features
16
"I need to manage many copies of data
on fragmented data infrastructure,
while handling adhoc requests from
data scientists."
• Need to provision and manange fragmented
data infrastructure
• Data processing does not scale to needs
• Serving features in production is hard

How Feast come to rescue ?
17
Data Engineer
Data scientist
MLE
Source:MLOps Tools Part 5: BigQuery + Memorystore vs. FEAST for Feature Store | Datatonic

Store: Feature registry
18
Entity
Datasouce
Feature view

Serve
19
get_historical_features get_online_features

Serve: Feature service
20
Feature view 1
Feature view 2
Very long
features retrieve ?

DEMO SCENARIO
21

Scenario
22
• As a Data Scientist (DS):
o Want to quickly experiment based on pre-calculate features
o So that I don't spend much time in duplicated works.
• As a Data Engineer:
o Want to select final data version
o So that I can integrate to data pipeline.
• As a Machine Learning Engineer:
o Want to quickly select latest version materialized from Data platform
o So that I can serve online APIs inferences.

As a Data Scientist (1) - Create and Register features (staging)
23
Source: What is a Feature Store?
(feast.dev)

As a Data Scientist (1) - Create and Register features (staging)
24
Step 1: Build feature
Step 2: Save to a location
Step 3: Registry to feature store

As a Data Scientist (2) - Reuse features for other training
25
Source: What is a Feature Store?
(feast.dev)

Feast UI
26

Feast UI
27

As a Data Engineer – Integrate to data pipeline (production)
28
Source: link

Feast: CI/CD pipeline and Auto import features
29

30

31

32

As a Machine Learning Engineer
33
Source: A State of Feast

As a Machine Learning Engineer
34
Get feature
service
Get features
Either get from request
body or feature list
Source: https://github.com/ElliotNguyen68/feast_epam.git

Future works
35

Datasource VectorDB
• Extend DataSource class for VectorDB such as Milvus, Pinecone, PGVector.
• [Alpha] Data quality monitoring (link)
36

Discussion
37

Strengths and weaknesses
• Standardized and Consistent Features: ensures features usage
across different models whose same definitions and
transformations are applied everywhere.
• Time-series Support: provide point-in-time correctness — a key
important factor in financial or transactional data.
• Integration with other tools: Feast integrates well with popular
machine learning frameworks such as Airflow, Dagster, MLFlow,
K8s, along with data and model management tools.
• Online and Offline Support: helpful for real-time versus batch
predictions, simplifies deploying and serving models by providing a
unified way to manage all feature data for both training and
prediction. Currently Feast support SnowFlake, Bigquerry,
Postgresql, MSQL, RedShift, Athena, DuckDB, Trino, Ibis,
Redis, DynamoDB, Cassandra, Hbase, Rockset, Hazecast, ...
• Data Versioning: easy to manage and track different versions of
features.
PROS
• Complexity: may be overkill for simple projects or teams just
getting started with machine learning.
• Minimal UI: offers minimal user interface options for exploring and
managing features.
• Limited Database Support: As of now, Feast supports only a limited
number of databases like Google BigQuery, Redshift, Snowflake,
Spark, and PostgreSQL.
• Documentation: While improving, the documentation for Feast is
still a bit sparse, particularly for complex deployments or advanced
use cases.
CONS
38

Extend beyond tabular data: Images and Text
39
Ideas
• Use Feast to manage the metadata of these feature vectors (like feature
names, data types, and statistical properties), not the actual raw image and
text data.
For image data
• Features could be raw pixel values, or some transformation of the images, such
as edges, textures, shapes. You can also make use of pre-trained Convolutional
Neural Networks (CNNs) to extract features.
For text data
• Features could be based on various Natural Language Processing (NLP)
techniques like BOW, TF-IDF, word embeddings, or even more advanced things
like sentence embeddings generated from models like BERT or GPT.
Source: link
Source: link

Keys takeaway
• Understanding the needs and motivations for feature stores in data operations and collaboration.
• Data scientists can quickly experiment based on pre-calculated features without spending time for data preparation.
• Data engineers have the ability to integrate with data pipelines correctly by choosing correct data version from features store.
• Machine Learning Engineers can serve online APIs inferences with consistent calculated features.
• Understanding the process of storing and serving data using Feast.
• Feast is easy to integrate with current MLOps system.
• Feast can extend for other DataSource such as VectorDB.
• Feast can extend beyond tabular data: Images and Text.
40

THANK YOU!
41

APPENDIX
42

Store: calculate feature (loan to income)
43

Registry's schema
44

Feast Feature Store - An In-depth Overview Experimentation and Application in Tabular data.pdf

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