Get your MLOps (Level 1) platform started and going fast.
Using a feature store to connect the DataOps and MLOps workflows to enable collaborative teams to develop efficiently.
A PHP script / API endpoint that will generate the Roman Catholic liturgical calendar for any given year, calculating the mobile festivities and the precedence of solemnities, feasts, memorials...
Build Recommender System with PyTorch + Redis + Elasticsearch + Feast + Triton + Flask. Vector Recall, DeepFM Ranking and Web Application.
A demo of Redis Enterprise as the Online Feature Store deployed on GCP with Feast and NVIDIA Triton Inference Server.
This repo contains a plugin for feast to run an offline store on Spark
A demo pipeline of using Redis as an online feature store with Feast for orchestration and Ray for training and model serving
Feast Client SDK for Node.js
This is a repository created to explore different tools and technologies related to feature stores to build and serve ML models.
Recommender systems became one of the essential areas in the machine learning field. Product recommendations are key to enhance customer exeperiance and help them to find the right product from huge corpus of products. When customer find the right product that are mostly like going to add the item to cart and which help in company revenue.
An implementation of a recommender system pipeline using PyTorch
Polish day off and feast utility classes
Searchable list of potential leavening agents to remove from your dwelling during Passover and the Feast of Unleavened Bread
Feast Feature Store for scaling customer churn model.
Example showing how to use denormalized to stream features into feast
Feast as a combinator.ml component
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