AWS Certified Developer - Associate

Professionals can use SageMaker-based technologies to develop models, train them using cloud infrastructure, tune configurations, deploy models, and monitor machine learning applications.

Understanding managed machine learning services can help organizations reduce infrastructure management requirements and focus more on building useful ML solutions.

Model Training and Evaluation

Training is the process of using historical data to develop a machine learning model. The quality of training data and the choice of algorithm can significantly influence model performance.

Datasets are commonly divided into training and validation or testing portions. This allows professionals to evaluate whether a model can generalize beyond the data used during training.

Important evaluation metrics depend on the problem. Classification may involve accuracy, precision, recall, or F1 score, while regression problems may use metrics such as mean absolute error or mean squared error.

Understanding model evaluation helps engineers determine whether a model is ready for deployment.

Hyperparameter Optimization

Machine learning models often require configuration of hyperparameters. These values can influence how the model learns and how well it performs.

Hyperparameter optimization involves testing different configurations to identify a suitable combination. AWS machine learning technologies can support automated tuning processes.

Machine learning engineers need to understand the relationship between model parameters, training data, evaluation metrics, and overall model performance.

Model Deployment