This course shows you how to use a range of AWS services to create intelligent end-to-end applications that incorporate ingestion, storage, preprocessing, machine learning (ML), and connectivity to an application client or server. The course is designed for data scientists looking for clear instruction on how to deploy locally developed ML applications to the AWS platform, and for developers who want to add machine learning capabilities to their applications using AWS services. Prerequisites include: Basic awareness of Amazon Simple Storage Service (S3), Elastic Compute Cloud (EC2), and Amazon Elastic MapReduce; as well as some knowledge of ML concepts like classification and regression analysis, model types, training and performance measures; and a general understanding of Python.

Understand how to use Amazon Web Service's best-in-class streaming analytics and ML tools
Learn about Amazon data pipelines: A very lightweight way to deploy an ML algorithm
Explore Redshift and RDS: Databases that stage input data or store model outputs
Discover Kinesis: A streaming data ingestion service that performs streaming analytical functions
Learn to apply streaming and batch analytical processing to prepare datasets for ML algorithms
Gain experience building ML models using Amazon Machine Learning and calling them using Python