3Scaling prototypes into ML models

• 3.1Building and training models

  Understand choosing ML framework and model architecture, modeling techniques per interpretability requirements, organizing training data (tabular/text/speech/image/video), ingesting file types (CSV/JSON/images/Hadoop/DB), training with SDKs (Vertex AI custom training, Kubeflow on GKE, AutoML, Tabular Workflows), reliable distributed training, hyperparameter tuning, troubleshooting training failures, and fine-tuning foundation models.

• 3.2Choosing training hardware

  Understand evaluating compute and accelerator options (CPU, GPU, TPU, edge devices) and distributed training with TPUs and GPUs (Reduction Server on Vertex AI, Horovod).