Search⌘ K
AI Features
Home
Courses
Grokking the AWS Certified Machine Learning Engineer - Associate

Model Selection and Feasibility

Explore strategies for choosing the right machine learning model based on problem type, data size, and operational constraints within AWS. Learn to balance interpretability and performance, leverage SageMaker built-in algorithms, managed AI services, and conduct feasibility assessments to ensure viable, scalable ML solutions on AWS.

We'll cover the following...

Selecting the right machine learning model is one of the most consequential decisions an ML engineer makes, and it is a recurring theme on the AWS Certified Machine Learning Engineer  Associate (MLA-C01) exam. The challenge is matching the model to the problem type, the data characteristics, and the systems operational constraints.

Amazon SageMaker is the primary AWS service for this workflow. It offers built-in algorithms such as Linear Learner and XGBoost, along with the flexibility to bring your own TensorFlow or PyTorch models. Beyond SageMaker, AWS provides managed AI services like Amazon Rekognition, Amazon Comprehend, and Amazon Polly, which can eliminate the need for custom model building entirely when the task is common. This lesson walks through the decision-making framework you need: model selection strategies, aligning data size and quality with model complexity, interpretability trade-offs, feasibility assessment, and constraint-based decision-making within the AWS ecosystem.

Model selection strategies

Choosing an algorithm begins with understanding three major model families and the scenarios where each excels. The decision process follows a clear sequence: identify the problem type (classification, regression, or clustering), assess data characteristics (size, structure, and feature types), and then evaluate performance and interpretability requirements.

The following are the three families and their practical positioning within SageMaker:

  • Linear models (SageMaker Linear Learner): These work best when the relationship between features and the target is approximately linear. They train quickly on CPU instances like ml.m5, require less data, and produce highly interpretable outputs through direct coefficient weights. Common use cases include binary classification and simple regression on structured datasets.

  • Tree-based models (SageMaker XGBoost): These handle nonlinear relationships, mixed feature types, and feature interactions effectively. XGBoost is a default choice for structured, tabular-data problems on AWS, running efficiently on ml.m5 ...