updated with project description

README.md

@@ -1,24 +1,31 @@
-# MLOps
-learning MLops
+# MLOps Rent Prediction
 
+## Overview
+This project aims to turn ML models developed in Jupyter Notebooks into production-ready, fully operational microservices deployable in containers.
 
-Goal: Turn ML models developed in Jupyter Notebook to production-ready fully operational microservices deployable in containers.
+## Getting Started
+### Prerequisites
+- Python 3.11
 
-Topics checklist:
+### Installation
+1. Clone the repository:
+   ```sh
+   git clone https://github.com/arvinsingh/MLOps.git
+   cd MLOps
+   ```
 
-+ Develop ML model in Jupyter Notebook.
-+ Turn the Jupyter Notebook code into a clean organized proper codebase.
-    - Structure
-    - Parameterize
-    - Logging
-    - Dependencies
-    - Automation using makefile
-    - Setup CI/CD using GitHub Actions for Testing & Deployment
-+ Designing APIs.
-    - Design principles
-    - Scaling & Managing asynchronous operations
-    - Handeling requests & responses.
-+ Containerizing application using Docker.
-    - Creating Docker images
-    - Multistage building process
-    - Establishing communication between containers for seemless interaction
+2. Install dependencies:
+   ```sh
+   make install
+   ```
+
+### Running the Application
+1. Train the model:
+   ```sh
+   make runner-builder
+   ```
+
+2. Run the inference service:
+   ```sh
+   make runner-inference
+   ```