Model Deployment is the process of making a trained model available for use in a production environment.
This involves:

1. Saving (serializing) the trained model.
2. Setting up an interface (like an API) for external systems to send data.
3. Hosting the model on servers, cloud, or edge devices.
4. Ensuring continuous performance through monitoring and retraining.

Types of Deployment:

• Online (Real-time) — Immediate prediction responses (e.g., chatbots, recommendation engines).
• Offline (Batch) — Periodic processing of large data sets (e.g., nightly analytics jobs).

After completing this module, I gained practical experience in:

1. Understanding Model Deployment’s Role

   • Recognizing how deployment transforms a static ML model into a real-world, value-generating solution.
   • Explaining the importance of production readiness and system integration.

2. Model Serialization Techniques

   • Exporting trained models using Pickle or Joblib for reuse and deployment.
   • Understanding how serialized models integrate into APIs or applications.

3. Building Interactive Applications with Streamlit

   • Developing web-based dashboards and applications for live model interaction.
   • Visualizing predictions and insights with user-friendly interfaces.

4. Containerization and Deployment Consistency

   • Learning why containerization is vital for reproducibility and scalability.
   • Understanding how Docker provides lightweight, portable containers for ML workflows.

5. Creating RESTful APIs with Flask

   • Designing robust, API-driven model deployment architectures.
   • Serving ML models as APIs for external system integration and automation.

6. Deploying Scalable Solutions

   • Combining Docker, Flask, and Streamlit to deploy complete ML applications.
   • Ensuring reliability, maintainability, and scalability in production.

• Gained an end-to-end understanding of how trained models move from notebooks to production.
• Explored the differences between APIs and web apps in model deployment.
• Learned how to design REST APIs for serving predictions programmatically.
• Understood how Streamlit enables rapid prototyping and visualization.
• Mastered Docker fundamentals — building images, managing containers, and ensuring deployment consistency.
• Studied best practices for secure model hosting, dependency management, and scaling.

Below is a simplified architecture showing how each component fits together:

          ┌────────────────────────┐
          │     Trained Model      │
          │   (Serialized via      │
          │    Pickle/Joblib)      │
          └──────────┬─────────────┘
                     │
             ┌───────▼────────┐
             │   Flask API    │
             │(Model Serving) │
             └───────┬────────┘
                     │
             ┌───────▼────────┐
             │   Streamlit    │
             │ (User Interface)│
             └───────┬────────┘
                     │
             ┌───────▼────────┐
             │   Dockerized   │
             │   Environment  │
             └────────────────┘

This modular architecture ensures:

• Streamlit provides intuitive visualization and interaction.
• Flask serves predictions via APIs.
• Docker guarantees environment consistency across systems.

• The Need for Model Deployment
• Introduction to Model Deployment
• Model Serialization
• Introduction to APIs
• Endpoints & Requests
• Handling Dependencies
• Securely Hosting a Deployed Model
• Architecture of Model Deployment
• Streamlit for Model Interaction
• Flask REST API Development
• Docker for Containerized Deployment

• This article covers various techniques for serialization and deserialization

• This book provides a detailed introduction to APIs along with illustrative examples

• This is the official documentation of Streamlit, which includes examples along with a conceptual guide

By completing this module, I developed a clear, hands-on understanding of ML model deployment, including:

• Turning models into accessible APIs or applications.
• Managing and scaling deployment with containerization.
• Ensuring that models are secure, portable, and maintainable in production.

This repository serves as a practical reference and documentation of those learnings.