Seamless Deployment of Machine Learning Applications on AWS with GitHub Actions: A Comprehensive Guide — PART 3

Part 3: Designing the Deployment Pipeline with GitHub Actions

1. CI/CD Principles for Machine Learning Applications

1.1 The Importance of CI/CD for ML

Continuous integration and deployment pipelines are especially important for machine learning applications because of the complexities involved in:

• Model Versioning: Managing changes in both code and model parameters.
• Dependency Management: Ensuring that library versions remain consistent across development, testing, and production.
• Data Integration: Seamlessly integrating data preprocessing and training pipelines.

Automating these processes minimizes human error, accelerates time-to-market, and ensures that production systems are updated in a controlled manner.

1.2 Unique Challenges of ML Pipelines

Unlike traditional web applications, ML workflows involve:

• Dynamic Data: New data often requires retraining and redeploying models.
• Resource-Intensive Testing: Model performance…