We can host this training at your preferred location. Contact us!

Explore Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models.

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.

WHO SHOULD ATTEND?

Experienced data scientists who are proficient in ML and deep learning fundamentals

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

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

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

Upcoming Trainings

Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

04 August 2024