AWS AI Practitioner: The ML Development Lifecycle

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Data scientists have discovered that machine learning (ML) is not successful when approached with traditional programming and coding methodologies and lifecycles. Although there are some similarities, ML development has unique pipeline components, model sources, production methods, and relevant services and features. In this course, you will examine components of an ML pipeline and sources of ML models. Next, discover methods for using a model in production and how to use Amazon SageMaker in an ML pipeline. Finally, learn about using SageMaker Data Wrangler, SageMaker Feature Store, and SageMaker Model Monitor in ML pipelines. This course is part of a collection that prepares you for the AIF-C01: AWS Certified AI Practitioner exam.