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AWS Authorized Training Course - Amazon SageMaker Studio for Data Scientists

Course Summary

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.

Prerequisites
We recommend that all attendees of this course have:
   • Experience using ML frameworks
   • Python programming experience
   • At least 1 year of experience as a data scientist responsible for training, tuning, and
     deploying models
   • AWS Technical Essentials digital or classroom training

THIS COURSE IS NOT ELIGIBLE FOR TRAINING BUNDLES.

Purpose
For data scientists who want to prepare, build, train, deploy, and monitor machine learning  (ML) models quickly.
Audience
Experienced data scientists who are proficient in ML and deep learning fundamentals
Role
Data Scientists
Skill level
Advanced
Style
Presentations | Hands-on Labs | Demonstrations |Discussions | Capstone
Duration
3 days
Related technologies
AWS Cloud

 

Course objectives
  • Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio

What you'll learn:

In this AWS AuthorizedTraining course, Amazon SageMaker Studio for Data Scientists, you'll learn:

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
    • 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 Pipeline   

AWS Authorized Training Course - Amazon SageMaker Studio for Data Scientists

real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
fully customized

Tailored Outlines

Once-size-fits-all doesn't apply to training teams. That's where we come in!

Dive in and learn more

When transforming your workforce, it’s important to have expert advice and tailored solutions. We can help. Tell us your unique needs and we'll explore ways to address them.

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