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2.2

Task 2.2: Train and refine models

Knowledge of:

  • Elements in the training process (for example, epoch, steps, batch size)
  • Methods to reduce model training time (for example, early stopping, distributed training)
  • Factors that influence model size
  • Methods to improve model performance
  • Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)
  • Hyperparameter tuning techniques (for example, random search, Bayesian optimization)
  • Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network)
  • Methods to integrate models that were built outside SageMaker AI into SageMaker AI

Skills in:

  • Using SageMaker AI built-in algorithms and common ML libraries to develop ML models
  • Using SageMaker AI script mode with SageMaker AI supported frameworks to train models (for example, TensorFlow, PyTorch)
  • Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart)
  • Performing hyperparameter tuning (for example, by using SageMaker AI automatic model tuning [AMT])
  • Integrating automated hyperparameter optimization capabilities
  • Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection)
  • Combining multiple training models to improve performance (for example, ensembling, stacking, boosting)
  • Reducing model size (for example, by altering data types, pruning, updating feature selection, compression)
  • Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)