AWS Certified Machine Learning - Specialty

Hyperparameter Optimization

Machine learning models often have hyperparameters that influence the training process. Selecting appropriate values can improve model performance.

Hyperparameter optimization involves testing different configurations and identifying suitable settings based on an evaluation metric.

AWS machine learning technologies can help automate this process. This is useful when testing a large number of possible configurations would otherwise require significant manual effort.

Understanding the relationship between hyperparameters, training data, model architecture, and evaluation results is an important machine learning skill.

Machine Learning Deployment

A machine learning model becomes useful when it can generate predictions for real-world applications. Deployment makes a trained model available to applications, users, or automated workflows.

Depending on the workload, models can support real-time inference or batch prediction.

Real-time inference is useful when applications need immediate predictions, while batch inference can be suitable for processing large quantities of data periodically.

Architects and engineers must consider latency, scalability, availability, resource usage, and cost when selecting deployment methods.

Monitoring Machine Learning Models

Machine learning systems need ongoing monitoring after deployment. Model performance can change when real-world data differs from training data.