01. Deployment Fundamentals

Transitioning from Development to Production: Key Steps

Successfully deploying a model requires moving from a development setting to a reliable, live production environment. Here are the fundamental steps involved:

1. Refactoring Code

   • Transition from Jupyter Notebooks to Python modules or scripts.
   • Develop a repeatable workflow that handles data processing, model retraining, and evaluation.
   • Use pipelines, which manage data preprocessing, training, and prediction, simplifying deployment.

2. Serializing the Model

   • Save the model's trained parameters to avoid retraining. This is commonly done using pickling in Python.

3. Setting Up an Execution Environment

   • Establish an environment for obtaining predictions on new data. This can range from manual scripts to automated cloud-based solutions.

Using these steps ensures that models maintain their evaluated performance when deployed, securing the return on investment in development efforts.

This lesson aims to provide the insights needed to optimize and run models effectively in production settings.