Model Selection and Feasibility for AWS ML Projects

Choosing the wrong machine learning model or failing to assess project feasibility early can lead to costly failures in production. This course provides a structured, step-by-step approach to evaluating machine learning viability and selecting the most effective models for your business needs using AWS infrastructure. You will transition from uncertainty to confidence by learning how to map business problems to ML tasks, evaluate model architectures, and run feasibility assessments. Through written guides and conceptual exercises, you will master the principles of model selection using SageMaker and pre-built AWS AI services, ensuring your deployments are both scalable and cost-effective. What you'll learn: - Analyze business requirements to determine machine learning feasibility and technical constraints. - Compare built-in SageMaker algorithms with custom models and pre-trained AWS AI services. - Evaluate model performance, latency, and resource trade-offs for production workloads. - Estimate and optimize AWS infrastructure costs for training and real-time hosting. - Incorporate modern foundation models and generative AI selection criteria into your decision framework. - Design validation strategies to prevent data leakage and ensure reliable model evaluation. The course begins with foundational definitions of feasibility, data readiness, and model evaluation metrics. You will then progress through structured frameworks for comparing algorithms, leveraging SageMaker features, and calculating total cost of ownership. This course is designed for aspiring AWS machine learning engineers, data scientists, and cloud architects who want a systematic approach to model selection. No advanced mathematical background or prior cloud engineering experience is required to get started. Start reading today to build a solid foundation in architectural decision-making for AWS machine learning.