This course covers part of the data preparation phase of the machine learning (ML) lifecycle.
In this course, you will learn how to collect, ingest, and store data for ML and artificial intelligence (AI) workloads. This course covers data fundamentals, such as how to recognize data types and formats and how to choose the right storage service based on cost, performance, and access patterns. You will use Amazon Web Services (AWS) services such as Amazon Simple Storage Service (Amazon S3), Amazon Kinesis, and AWS Glue to ingest, extract, and merge data. You will also configure vector databases with Amazon OpenSearch Service and manage features with Amazon SageMaker Feature Store.
