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Amazon SageMaker Studio for Data Scientists
Duration:
3 Day
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$2095
USD
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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.
- Course level: Advanced
- Duration: 3 days
Activities
This course includes presentations, hands-on labs, demonstrations, discussions, and a capstone project.
This course is available in the following formats:
Duration: 3 Day
Duration: 3 Day
| Call 800-798-3901 to enroll in this class! |
In this course, you will learn to:
- Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio
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
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
Module 3: Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
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
- SageMaker Jumpstart
Module 4: Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
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
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
- Using AWS Glue interactive sessions
- Using SageMaker Processing with custom scripts
Module 3: Model Development
- SageMaker training jobs
- Built-in algorithms
- Bring your own script
- Bring your own container
- SageMaker Experiments
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
- SageMaker Jumpstart
Module 4: Deployment and Inference
- SageMaker Model Registry
- SageMaker Pipelines
- SageMaker model inference options
- Scaling
- Testing strategies, performance, and optimization
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
- 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
- Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- Hands-On Lab: Inferencing with SageMaker Studio
- 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
- Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
- Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
- Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
- Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
- Hands-On Lab: Inferencing with SageMaker Studio
Experienced data scientists who are proficient in ML and deep learning fundamentals