Hopsworks - Data-Intensive AI platform with a Feature Store

🌀 𝗧𝗵𝗲 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝟳-𝗦𝘁𝗲𝗽𝘀 𝗠𝗟𝗢𝗽𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 | 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗘 & 𝗠𝗟𝗢𝗽𝘀 for free by designing, building and deploying an end-to-end ML batch system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 2.5 𝘩𝘰𝘶𝘳𝘴 𝘰𝘧 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 & 𝘷𝘪𝘥𝘦𝘰 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.

Compute and store real-time features for crypto trading using Bytwax (stream processing) and Hopsworks (Feature Store)

Develop and deploy a real-time feature pipeline in Python, using Bytewax 🐝 and Hopsworks Feature Store.

Python - Java/Scala API for the Hopsworks feature store

Learn to build a modular real-time feature pipeline, so you avoid Offline-Online Feature Skew, and your deployed ML models work as expected.

A simple app to help you find relevant machine learning papers to read.

Backfill historical OHLC feature in a Feature Store (Hopsworks) using an orchestration tool (Prefect).

Hopsworks.ai Terraform provider

Predicting Snow Conditions in Passo Tonale (Trento, Italy)

Terraform module that creates the required cloud resources for Hopsworks.ai clusters on AWS and AZURE.

This is a batch-inference service of NYC Taxi rides 🚗.

Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.

FastRide: NYC Taxi Trip Duration Prediction employs machine learning techniques to accurately predict the duration of taxi trips in New York City, helping transportation businesses optimize operations and enhance customer satisfaction.

Modern machine learning project development - This end-to-end project implementation provides the real-time delay updates to logistic companies. It uses MLflow for model tracking and management, Hopsworks Feature Store for storing and managing the dataset, and Streamlit for building an interactive web application to predict truck delays.

Serverless Machine Learning for Titanic Dataset

The work uses Hopsworks for Feature store and creating Feature groups for Machine learning modeling

Go SDK to interact with the Hopsworks API