IMPLEMENTING CI/CD PIPELINES FOR MACHINE LEARNING MODELS: BEST PRACTICES AND CHALLENGES

Continuous Integration (CI) and Continuous Deployment/Delivery (CD) practices have become indispensable in modern machine learning (ML) pipelines, aiming to streamline the development, testing, and deployment of ML models. This review paper explores best practices, challenges, and solutions in implementing CI/CD pipelines specifically tailored for ML. Key areas addressed include modular pipeline design for flexibility and reusability, automated data validation to ensure data quality, strategies for reproducibility in experiments, scalability considerations for handling large datasets and complex models, integration challenges with existing systems, security measures to protect sensitive data, and the importance of collaboration and documentation in enhancing team efficiency and knowledge sharing. By addressing these aspects, organizations can optimize their ML workflows, accelerate model deployment, and maintain robustness and reliability in their AI-driven applications.