Continuous integration (CI) builds in machine learning (ML) projects tend to run longer than in other projects, yet little is known about the longest builds, which consume most CI time. This paper characterizes the longest builds in ML projects, analyzes how CI workflow properties are associated with them, and evaluates whether they can be predicted before execution. We study 269,930 GitHub Actions builds from 107 open-source ML projects. The top 18% longest builds account for 80.30% of total build time and fail in 39.09% of cases. Build duration differs significantly across ML project types. The framework and infrastructure ML projects have the longest upper tail and produce 98% of the longest builds, whereas projects that use existing ML libraries have the lowest median build duration. The longest builds are most strongly associated with the recent history of the same workflow, as the duration of its last build and the median duration of the project's last 20 builds show large effect sizes. The longest builds are more consistently associated with CI workflow structure than with configuration. The number of jobs, control flow complexity, coupling complexity, and maximum steps per job show medium effect sizes, whereas caching, matrix settings, and Docker builds show small or negligible effects. Framework-specific workflows have the largest percentage of builds among long builds. Platform-specific workflows produce the largest number of the longest builds. Finally, 47 features describing build history, workflow structure, and the ML project outperform a traditional baseline with all four classifiers. In a chronological evaluation, the best model (XGBoost) reaches an F1-score of 0.775, compared with 0.682 for the baseline, and an AUC-ROC of 0.955. These results show that build history and CI workflow structure help identify the longest builds in ML projects before they run.

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