Course Overview
Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor. The three-day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.
Who should attend
- Experienced data scientists who are proficient in ML and deep learning fundamentals.
- Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
Prerequisites
We recommend that all students complete the following AWS course prior to attending this course:
We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:
- The Machine Learning Pipeline on AWS (ML-PIPE)
- Deep Learning on AWS
Course Objectives
- Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
- Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
- And much more
Course Content
- Amazon SageMaker Setup and Navigation
- Data Processing
- Model Development
- Deployment and Inference
- Monitoring
- Managing SageMaker Studio Resources and Updates
- Capstone
English