Practical Data Science with Amazon SageMaker
This is a 1-day workshop focused on hands-on learning with Amazon SageMaker, with minimal lectures and more practical exercises.
The Practical Data Science with Amazon SageMaker course is designed to help you understand how to work effectively with data scientists and develop applications that use machine learning (ML). Over the course of one day, you will learn the essential processes that data scientists follow to create ML solutions using Amazon Web Services (AWS) and Amazon SageMaker.
The training includes presentations, hands-on labs, and demonstrations to give you practical experience. By the end of the course, you will be able to discuss the advantages of various machine learning types for addressing business challenges, describe the roles and responsibilities within a team that builds and deploys ML systems, and explain how data scientists utilize AWS tools to tackle common business issues. You will also learn the steps involved in preparing data, training ML models, evaluating and tuning these models, and deploying a model to generate predictions. Additionally, the course will cover the challenges of operationalizing ML models and help you match AWS tools to their specific ML functions.
This course is aimed at Development Operations (DevOps) engineers and application developers. To get the most out of the training, it is recommended that participants have a basic understanding of AWS Technical Essentials, some knowledge of Python programming, and a foundational grasp of statistics.