Amazon Sagemaker Studio for Data Scientists

• 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 lifecycl

Level: Advanced
Type: Classroom (virtual and in person)
Length: 3 days

• 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

• Course outline Day 1 Module 1: Amazon SageMaker Studio Setup • JupyterLab Extensions in SageMaker Studio • Demonstration: SageMaker user interface demo Module 2: Data Processing • Using SageMaker Data Wrangler for data processing • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler • Using Amazon EMR • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR • Using AWS Glue interactive sessions • Using SageMaker Processing with custom scripts • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK • SageMaker Feature Store • Hands-On Lab: Feature engineering using SageMaker Feature Store Module 3: Model Development • SageMaker training jobs • Built-in algorithms • Bring your own script • Bring your own container • SageMaker Experiments • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models Day 2 Module 3: Model Development (continued) • SageMaker Debugger • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger • Automatic model tuning • SageMaker Autopilot: Automated ML • Demonstration: SageMaker Autopilot • Bias detection • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability • SageMaker Jumpstart Module 4: Deployment and Inference • SageMaker Model Registry • SageMaker Pipelines • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio • SageMaker model inference options Amazon SageMaker Studio for Data Scientists AWS Classroom Training © 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved. • Scaling • Testing strategies, performance, and optimization • Hands-On Lab: Inferencing with SageMaker Studio Module 5: Monitoring • Amazon SageMaker Model Monitor • Discussion: Case study • Demonstration: Model Monitoring Day 3 Module 6: Managing SageMaker Studio Resources and Updates • Accrued cost and shutting down • Updates Capstone • Environment setup • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler • Challenge 2: Create feature groups in SageMaker Feature Store • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments • (Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimization • Challenge 5: Evaluate the model for bias using SageMaker Clarify • Challenge 6: Perform batch predictions using model endpoint • (Optional) Challenge 7: Automate full model development process using SageMaker Pipelin

We recommend that all students complete the following AWS course prior to attending this course:

• AWS Technical Essentials (1–day AWS instructor led 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 (4–day AWS instructor led course)
• Deep Learning on AWS (1–day AWS instructor led course)

• This course is offered in English.
• We regularly update our courses based on customer feedback and AWS service updates. As a result, course content may vary between languages while we localize these updates.