AWS Certified Developer - Associate

Data Preparation

High-quality data is one of the most important requirements for successful machine learning. Real-world datasets may contain missing values, duplicate records, inconsistent formats, outliers, or irrelevant information.

Data preparation involves cleaning and transforming raw information into a format suitable for machine learning. Feature engineering can also be used to create or select useful variables that help models make better predictions.

AWS provides cloud services that can support data storage, processing, transformation, and integration. Amazon S3, for example, can provide scalable object storage for machine learning datasets.

Professionals need to understand how data preparation affects model quality and operational performance.

Machine Learning Model Development

Model development involves selecting an appropriate algorithm, preparing training data, and configuring the training process.

Different machine learning problems require different approaches. Classification can be used to predict categories, regression can be used to estimate numerical values, and clustering can identify groups within datasets.

Machine learning engineers need to evaluate models using appropriate metrics and understand whether the model meets the business objective.

They also need to consider factors such as training time, model complexity, scalability, and resource requirements.

Amazon SageMaker

Amazon SageMaker is an important AWS service for building, training, deploying, and managing machine learning models. It provides capabilities that can support different stages of the ML lifecycle.