Transitioning to ML engineering requires automating workflows for continuous integration and continuous deployment (CI/CD). Skills in tools like Jenkins, GitLab CI, or CircleCI tailored for ML pipelines (MLOps) ensure faster iterations, reproducibility, and stable releases. Understanding how to integrate ML workflows into CI/CD pipelines is critical.
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