Continuous Integration (CI): CI is the practice of continuously merging code changes from multiple developers into a shared repository, ensuring that the codebase is always up-to-date and consistent. In the context of MLOps, CI involves integrating new ML feature/training/inference pipeline code, updating these ML pipelines. Automated testing of feature/training/inference pipelines and feature functions helps ensure that the integrated changes do not introduce bugs or negatively impact the performance of the ML models.
Continuous Deployment (CD): CD is the practice of automatically deploying the integrated and tested features or models to production feature stores or production model registries. CD can also be integrated with feature/model monitoring to enable the automation of model retraining and updating when new data is available or when the performance of the models degrades.
CI/CD for MLOps helps maintain the reliability, reproducibility, and scalability of ML systems by automating the integration, testing, and deployment processes. This enables organizations to deliver high-quality ML models and features more quickly and efficiently, leading to faster innovation and better decision-making.