ML Model Development — AWS Certified Machine Learning Engineer - Associate

Official references for this objective

• Amazon Web Services — Types of Algorithms - Amazon SageMaker AI

  If your target values are categorical (mathematically discrete), then you have a classification problem .

• Amazon Web Services — What is Overfitting? - Overfitting in Machine Learning Explained - AWS

  it may may learn to use grass as a feature for classification, and may not recognize a dog inside a room

• Amazon Web Services — Built-in algorithms and pretrained models in Amazon SageMaker - Amazon SageMaker AI

  Based on historical data for a behavior, predict future behavior: predict sales on a new product based on previous sales data. Tabular Use the SageMaker

• Amazon Web Services — Configure and Launch a Hyperparameter Tuning Job - Amazon SageMaker AI

  RandomSeed – A value used to initialize a pseudo-random number generator. Setting a random seed will allow the hyperparameter tuning search strategies to produce more

• Amazon Web Services — Create a Hyperparameter Optimization Tuning Job for One or More Algorithms (Console) - Amazon SageMaker AI

  Hyperband can only be used with iterative algorithms, or algorithms that run steps in iterations, such as XGBoost or Random Cut Forest

• Amazon Web Services — Amazon SageMaker Debugger - Amazon SageMaker AI

  Receive texts and emails and stop training jobs when training issues are found using Use Debugger built-in actions for rules

• Amazon Web Services — Automated ML, no-code, or low-code - Amazon SageMaker AI

  For a UI-based, no-code AutoML experience, new users should use the Amazon SageMaker Canvas application in Amazon SageMaker Studio

• Amazon Web Services — Content Domain 2: ML Model Development - AWS Certified Machine Learning Engineer - Associate

  Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)

• Amazon Web Services — Run a Warm Start Hyperparameter Tuning Job - Amazon SageMaker AI

  The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to

• Amazon Web Services — SageMaker Autopilot - Amazon SageMaker AI

  Autopilot helps users understand how models make predictions by automatically generating reports that show the importance of each individual feature.