AWS-SM | Amazon SageMaker Studio for Data Scientists
 
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.
 
Objectives
In this course, you will learn to:
Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio
 
Intended audience
This course is intended for:
Experienced data scientists who are proficient in ML and deep learning fundamentals
 
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
 
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
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
 
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Debido a las constantes actualizaciones de los contenidos de los cursos por parte del fabricante, el contenido de este temario puede variar con respecto al publicado en el sitio oficial, sin embargo, Netec siempre entregará la versión actualizada de éste.