Description
Continuous Integration and Continuous Deployment (CI/CD) for AI models presents unique challenges that go beyond traditional software deployment pipelines. While conventional CI/CD focuses on code integration and application deployment, AI model pipelines must handle additional complexities including data versioning, model training, evaluation metrics, and specialized deployment requirements.
Machine learning operations (MLOps) demands a sophisticated CI/CD approach that can manage not just code, but also data dependencies, model artifacts, and performance metrics. Here’s how to build robust CI/CD pipelines specifically designed for AI model development and deployment, ensuring reproducibility, quality, and efficiency throughout the ML lifecycle.
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