Description:

In this assignment, you will integrate a machine learning model or application with real-world APIs and external systems. The goal is to enhance your model’s capabilities by interacting with live data from external sources, such as third-party APIs, databases, or web services. This project will help you understand how to make your applications more dynamic by consuming and processing data from real-world environments, enabling use cases like automated data retrieval, live predictions, or enriching your model’s functionality.

Key Objectives:

1. API Consumption: Learn how to interact with external APIs to retrieve live data, process it, and use it as input for your machine learning model or application.
2. Data Integration: Integrate the data from third-party systems (e.g., databases, web services) into your model’s workflow for real-time predictions or enhanced decision-making.
3. Authentication & Security: Implement proper authentication and security measures (e.g., API keys, OAuth) to securely interact with external systems.
4. Error Handling & Resilience: Develop robust error handling to deal with issues like API rate limits, connection timeouts, or invalid data from external sources.
5. End-to-End Workflow: Build an end-to-end workflow that takes external data, processes it through your model, and returns meaningful outputs or takes actions based on predictions.
6. Documentation & Testing: Test the integration thoroughly, document the API usage and integration steps, and ensure the system works reliably in a production-like environment.

By the end of this assignment, you will gain practical experience in building systems that rely on external data, making your application more interactive, real-time, and ready for real-world deployment.