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Merged
franciscojavierarceo merged 11 commits into from
Dec 21, 2024
Merged

feat: Snyk vulnerability issues fix. #4867

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86efa5e
Update README.md
franciscojavierarceo Dec 19, 2024
00c397f
chore: Update quickstart.md
franciscojavierarceo Dec 19, 2024
74a1950
fix: java/serving/pom.xml & java/pom.xml to reduce vulnerabilities
snyk-bot Dec 19, 2024
8b31eab
fix: sdk/python/feast/ui/package.json & sdk/python/feast/ui/yarn.lock…
snyk-bot Dec 19, 2024
7d9eeea
Feature/lrangine master (#6)
lokeshrangineni Dec 20, 2024
85d41ac
Feature/lrangine master (#7)
lokeshrangineni Dec 20, 2024
45c777f
updating netty library
lokeshrangineni Dec 20, 2024
93b0f36
updating netty library
lokeshrangineni Dec 20, 2024
735f800
Updating aws java sdk libraries.
lokeshrangineni Dec 20, 2024
cde8bcb
Adding verbose logs to debug the sudden failure of tests without any …
lokeshrangineni Dec 20, 2024
e6be70d
Adding verbose logs to debug the sudden failure of tests without any …
lokeshrangineni Dec 20, 2024
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chore: Update quickstart.md 
Signed-off-by: lrangine <19699092+lokeshrangineni@users.noreply.github.com>
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@franciscojavierarceo @lokeshrangineni
franciscojavierarceo authored and lokeshrangineni committed Dec 20, 2024
commit 00c397fd1c409b96f56bf64c851c4c9572aa1481
3 changes: 3 additions & 0 deletions docs/getting-started/quickstart.md
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Expand Up @@ -10,6 +10,9 @@ Feast (Feature Store) is an open-source feature store designed to facilitate the

* *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](../README.md)

## Prerequisites
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