What is Model Deployment

Model deployment is the process of taking a trained machine learning model and making it available for real world use, so it can generate predictions on new, live data.

A model sitting in a notebook, no matter how accurate, provides no real value until it is deployed where actual users or systems can access it.

Deployment Method 1: REST API with Flask

• Save the trained model as a file using a library like pickle or joblib.
• A Flask application loads this saved model when it starts.
• Create a/predict endpoint that accepts incoming data.
• Other applications send data to this endpoint and receive predictions in the response.

Deployment Method 2: Cloud Platforms

• The trained model is uploaded to a platform like AWS, Google Cloud, or Azure.
• The platform hosts the model and automatically manages scaling and availability.
• The platform generates an endpoint URL that other systems can call to get predictions.
• No manual server setup or maintenance is required from the developer.

Deployment Method 3: Containerization with Docker

• The model, code, and all dependencies are packaged together into a single container.
• A Dockerfile defines exactly how to build this container.
• The container runs identically across different systems, avoiding environment mismatches.
• You can then deploy this container to any cloud service or server that supports Docker.

Deployment Method 4: Edge Deployment

• The trained model is converted into a lightweight format, using tools like TensorFlow Lite.
• This lightweight model is installed directly onto a device, like a smartphone or IoT sensor.
• Predictions happen locally on the device, without needing an internet connection.
• This method is ideal for applications needing instant, real time responses.