Practical Data Science with Amazon SageMaker

Artificial intelligence and machine learning (AI/ML) are becoming mainstream. In this course, you will spend a day in the life of a data scientist so that you can collaborate efficiently with data scientists and build applications that integrate with ML. You will learn the basic process data scientists use to develop ML solutions on Amazon Web Services (AWS) with Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and labs.                                                                  

Discuss the benefits of different types of machine learning for solving business problems

• Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
• Explain how data scientists use AWS tools and ML to solve a common business problem
• Summarize the steps a data scientist takes to prepare data
• Summarize the steps a data scientist takes to train ML models
• Summarize the steps a data scientist takes to evaluate and tune ML models
• Summarize the steps to deploy a model to an endpoint and generate predictions
• Describe the challenges for operationalizing ML models
• Match AWS tools with their ML function

AWS Technical Essentials

• Entry-level knowledge of Python programming
• Entry-level knowledge of statistics
• Recommended previous knowledge

Development Operations (DevOps) engineers

• Application developers