4 repositorios
Awesome GitHub RepositoriesML Feature Stores
Systems for storing and serving pre-computed feature vectors for low-latency machine learning predictions.
Distinct from Machine Learning: The candidates are generic ML lists; this is specifically about the storage and serving layer for features.
Explore 4 awesome GitHub repositories matching data & databases · ML Feature Stores. Refine with filters or upvote what's useful.
Awesome ML Feature Stores GitHub Repositories
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- aws/amazon-sagemaker-examples
aws/amazon-sagemaker-examples
10,958Ver en GitHubThis repository is a collection of Jupyter notebooks providing reference implementations and templates for building, training, and deploying machine learning models using Amazon SageMaker. It serves as an example library for implementing model architectures and automating the machine learning lifecycle. The library provides practical patterns for machine learning training, data engineering, and model deployment. It includes implementation guides for MLOps, including workflows for model monitoring, lineage tracking, and hyperparameter tuning. The examples cover a broad range of capabilities i
Provides guides for cleaning, labeling, and transforming large datasets and managing feature stores.
Jupyter Notebookawsdata-sciencedeep-learning - tporadowski/redis
tporadowski/redis
9,987Ver en GitHubRedis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations
Stores and serves pre-computed feature vectors to provide low-latency data for real-time ML predictions.
Credisredis-for-windowsredis-msi-installer - redis/redisinsight
redis/RedisInsight
8,556Ver en GitHubRedisInsight is a graphical user interface and management tool for browsing, analyzing, and administering Redis databases. It provides a visual environment for exploring key-value data structures, managing database instances, and performing data analysis across different operating systems and deployments. The tool distinguishes itself by providing dedicated visual managers for complex operations, including a vector database manager for configuring embeddings and similarity searches, a query workbench for executing raw commands and Lua scripts, and a performance monitoring dashboard for tracki
Retrieves pre-computed feature values with sub-millisecond latency for high-performance live model decisioning.
TypeScriptdatabase-guiredisredis-gui - alteryx/featuretools
alteryx/featuretools
7,658Ver en GitHubFeaturetools is an automated feature engineering library and data transformation framework written in Python. It automatically generates machine learning feature vectors from multi-table datasets by applying synthesis patterns to relational and timestamped data. The system functions as a distributed feature synthesis engine, allowing the process of creating feature vectors to scale across multiple cores or clusters to handle large-scale datasets. The library supports the synthesis of multi-table datasets, time series feature generation, and the creation of custom machine learning primitives
Generates a wide range of pre-computed feature vectors for use in machine learning models.
Python
