You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs.
This course is for individuals who seek an understanding of how to manage, optimize, and predict costs as you run workloads on AWS. You learn how to implement architectural best practices, explore cost optimization strategies, and design patterns to help you architect ...
In this course, you will learn best practices for designing and using cloud-based video workflows. It covers important concepts related to video processing and delivery, the variables that can impact migration decisions, and real-world examples of hybrid and cloud use cases for ...
In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a ...
Architecting on AWS is for solutions architects, solution-design engineers, and developers seeking an understanding of AWS architecting. In this course, you will learn to identify services and features to build resilient, secure and highly available IT solutions on the AWS ...