HOW TO DESIGN A FEATURE STORE FOR YOUR MLOPS PIPELINE | ML SYSTEM DESIGN

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Hi, thanks for watching our video about Feature Store
In this video we’ll walk you through:
- Feast
- Uber MichelAngelo
- Feature Store
- Optimization Technique Deep Learning
- TECTON
- MLOPS
- APORIA
- ML PIPELINES
- MLOPS
- GOOGLE LAMDA

Originally developed as an open-source feature store by Go-JEK, Feast has been taken on by Tecton to be a minimal, configurable feature store. You can connect in different online/offline data stores and it can run on any platform. Feature engineering is done outside of Feast.
Spotify built their own ML platform that leverages TensorFlow Extended (TFX) and Kubeflow. They focus on designing and analyzing their ML experiments instead of building and maintaining their own infrastructure, resulting in faster time from prototyping to production.
The mother of feature stores. Michelangelo is an end-to-end ML platfom and Palette is the features store. Features are defined in a DSL that translates into Spark and Flink jobs. Online FS is Redis/Cassandra. Offline is Hive.

One of the first feature stores from 2018, orginially called Zipline and part of the Bighead ML Platform. Feature engineering is using a DSL that includes point-in-time correct training set backfills, scheduled updates, feature visualizations and automatic data quality monitoring.

Other Feature Stores:
* Databricks Feature Store
* Vertex AI
* Iguazio
* AbacusAI

chatgpt,
gpt4,
google AI,
Google Lamda,
Google PALM,
artificial intelligence,
Machine learning,
transformer models,
deep learning

TIMESTAMPS
0:00 Intro
0:54 Feature store Intro (Tecton Video)
6:48 High Level Design
7:53 Offline vs Online Features
8:57 Key Components
10:36 Feature Store Design
11:36 Naive Solution
12:15 Architecture
15:02 Point In Time
16:00 Tile Sliding Window Approach
19:05 Serving
20:28 Tecton API

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