10 Things That Can Go Wrong with ML Projects
summary
Lists ten concrete failure modes for ML projects and maps each to remedies: start with a prototype and baseline, avoid jumping straight to heavy models, expect training to take days or weeks unless using serverless training on Vertex AI or Cloud TPUs, handle class imbalance with class weighting or SMOTE, try BigQuery ML or AutoML for quick baselines, use What‑If Tool and TensorFlow Model Analysis for slices, create model cards, run Vertex Pipelines, monitor drift and autoscale inference with Vertex Prediction.
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chapters (10)
- 0:00ML project challenges
- 0:54Aligning ML with business goals
- 3:19Choosing ML problem type
- 5:01Simple ML prototypes
- 7:29Slow model training
- 8:55Imbalanced datasets
- 13:07Stubborn model accuracy
- 16:56Fairness and transparency
- 22:09ML operations and drift
- 25:01Model inference at scale
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