Proposal Demo Week 4

contributions/demo/week4/jkuo-vanjav/README.md

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+**The pull request must both contain a README.md and have description following this template:**
+
+**The README.md file must be located in the directory**:
+
+`contributions/demo/week4/jkuo-vanjav/README.md`
+
+# Assignment Proposal
+
+## Title
+
+Feast in MLOps
+
+## Names and KTH ID
+
+  - Josephine Kuo [email protected]
+  - Vanja Vidmark [email protected]
+
+## Deadline
+
+- Week 4
+
+## Category
+
+- Demo
+
+
+## Description
+We will demonstrate how to use the feature store “Feast”. 
+
+We will showcase how to deploy a local feature store, build a training set using time series features and how to retrieve training data by getting historical data. This solves the problem of “point in time join” which is the problem of getting features that are accurate at a specific point in time. 
+
+**Relevance**
+
+Feast is an open-source feature store designed for managing and serving machine learning features. It helps streamline the process of feature engineering and provides a centralized repository for storing and managing features used in machine learning models.