Notebook Examples

Below is a compilation of tutorials focused on understanding and utilizing Feature Stores. You can find the raw notebook files in our tutorials repository.

Quick start examples

Notebook Examples

Jupyter Notebook	Description
Feature store querying	* Ingestion of data. * Querying and exploration of data.
Feature store quickstart	* Ingestion of data. * Querying and exploration of data.
Schema enforcement and schema evolution	* Schema evolution allows you to easily change a table’s current schema to accommodate data that is changing over time. * Schema enforcement, also known as schema validation, is a safeguard in Delta Lake that ensures data quality by rejecting writes to a table that don’t match the table’s schema.
Storage of medical records in feature store	Example to demonstrate storage of medical records in Feature Store.

Big data operations using OCI DataFlow

Notebook Examples

Jupyter Notebook	Description
Big data operations with feature store	* Ingestion of data using Spark Magic. * Querying and exploration of data using Spark Magic.

Streaming operations using Spark Streaming

Notebook Examples

Jupyter Notebook	Description
Streaming operations with feature store	* Ingestion of data using spark streaming. * Modes of ingestion: COMPLETE and APPEND.

LLM Use cases

Notebook Examples

Jupyter Notebook	Description
Embeddings in Feature Store	* Embedding feature stores are optimized for fast and efficient retrieval of embeddings. This is important because embeddings can be high-dimensional and computationally expensive to calculate. By storing them in a dedicated store, you can avoid the need to recalculate embeddings for the same data repeatedly.
Synthetic data generation in feature store using OpenAI and FewShotPromptTemplate	* Synthetic data is artificially generated data, rather than data collected from real-world events. It’s used to simulate real data without compromising privacy or encountering real-world limitations.
PII Data redaction, Summarise Content and Translate content using doctran and open AI	* One way to think of Doctran is a LLM-powered black box where messy strings go in and nice, clean, labelled strings come out. Another way to think about it is a modular, declarative wrapper over OpenAI’s functional calling feature that significantly improves the developer experience.
OpenAI embeddings in feature store	Embedding feature stores are optimized for fast and efficient retrieval of embeddings. This is important because embeddings can be high-dimensional and computationally expensive to calculate. By storing them in a dedicated store, you can avoid the need to recalculate embeddings for the same data repeatedly.