# Tutorials

- [Sample use-case tutorials](/tutorials/tutorials-overview.md)
- [Driver ranking](/tutorials/tutorials-overview/driver-ranking-with-feast.md): Making a prediction using a linear regression model is a common use case in ML. This model predicts if a driver will complete a trip based on features ingested into Feast.
- [Fraud detection on GCP](/tutorials/tutorials-overview/fraud-detection.md): A common use case in machine learning, this tutorial is an end-to-end, production-ready fraud prediction system. It predicts in real-time whether a transaction made by a user is fraudulent.
- [Real-time credit scoring on AWS](/tutorials/tutorials-overview/real-time-credit-scoring-on-aws.md): Credit scoring models are used to approve or reject loan applications. In this tutorial we will build a real-time credit scoring system on AWS.
- [Driver stats on Snowflake](/tutorials/tutorials-overview/driver-stats-on-snowflake.md): Initial demonstration of Snowflake as an offline+online store with Feast, using the Snowflake demo template.
- [Validating historical features with Great Expectations](/tutorials/validating-historical-features.md)
- [Building streaming features](/tutorials/building-streaming-features.md)
- [Retrieval Augmented Generation (RAG) with Feast](/tutorials/rag-with-docling.md)
- [RAG Fine Tuning with Feast and Milvus](/tutorials/rag-retriever.md)
- [MCP - AI Agent Example](/tutorials/mcp_feature_store.md)