This repository provides a comprehensive collection of tutorials for the Hopsworks platform, targeting machine learning engineers and data scientists. It aims to simplify the process of developing, deploying, and managing ML models in production, with a particular focus on leveraging Hopsworks' Feature Store capabilities for various use cases.

How It Works

The tutorials are designed to be run within Jupyter notebooks, either on the managed Hopsworks platform (app.hopsworks.ai) or by connecting to a Hopsworks instance using the hopsworks Python client library. This client facilitates authentication and interaction with the Hopsworks Feature Store, enabling users to access and manage features for their ML pipelines. The tutorials cover a wide range of ML applications, from real-time fraud detection to LLM-based assistants and time-series forecasting.

Quick Start & Requirements

• Install: !pip install -U 'hopsworks[python]' --quiet (within a notebook)
• Prerequisites: A Hopsworks account (app.hopsworks.ai). Familiarity with Machine Learning and Python is recommended.
• Resources: Requires connection to a Hopsworks instance.
• Docs: Hopsworks Documentation

Highlighted Details

• Extensive coverage of real-time, batch, and LLM AI systems.
• Demonstrates integration with numerous MLOps tools and data sources (Airflow, GCP, Azure SQL, BigQuery, Snowflake, LangChain, Weights & Biases, etc.).
• Includes API examples for Feature Store operations like vector similarity search and embeddings.
• Tutorials span diverse domains including fraud detection, recommendation systems, time-series analysis, and AML.

Maintenance & Community

• Feedback and comments are welcomed via the Support Forum, GitHub repository, or email (info@hopsworks.ai).

Licensing & Compatibility

• The repository itself appears to be under a permissive license, but the underlying Hopsworks platform has its own terms of service. Compatibility for commercial use depends on the Hopsworks platform's licensing.

Limitations & Caveats

• Running the tutorials requires an active Hopsworks account and potentially a connection to a Hopsworks instance, which may involve platform-specific terms.