Amazon SageMaker Studio for Data Scientists
Please not that this is not a prep course for any AWS certification!
This document contains links mentioned in the “Amazon SageMaker Studio for Data Scientists” course.
Module 0: Course Overview
Full name: Amazon SageMaker Studio for Data Scientists
Product code: 300-MLASMS-13
These links are based on the course version 1.3.20.
Online and classroom course options: Amazon SageMaker Studio for Data Scientists
The official course material: eVantage Bookshelf (VitalSource)
Additional material: Skill Builder
AWS Service Documents:
Module 1: Amazon SageMaker Studio Setup
Websites
- Amazon SageMaker
- Use Amazon SageMaker Notebooks
- Compare Amazon SageMaker Notebooks
- Where you can create a notebook job
- Amazon Elastic File System (EFS)
- Add External Git Repositories to SageMaker Studio Notebooks
- Dive Deep into Amazon SageMaker Studio Notebook Architecture
- Customize Amazon SageMaker Studio Using Lifecycle Configurations
- Collaboration with shared spaces
- Share ML Models and Notebooks with SageMaker JumpStart
- Customizations - SageMaker Studio Admin Guide
- 10 Jupyter Lab Extensions to Boost Your Productivity
- Amazon Q - Generative AI Assistant
- Accelerate your Software Development Lifecycle with Amazon Q
- Q Language IDE Support
- Onboard to Amazon SageMaker Quick Start
- Automate a Centralized Deployment of Amazon SageMaker Studio with AWS Service Catalog
- Granting SageMaker Studio Permissions Required to Use Projects
- Create SageMaker Projects with Image Building CI/CD Pipelines
- SageMaker JumpStart pretrained models
- IAM Best Practices – Grant Least Privilege
- Configuring SageMaker Studio for Teams with Resource Isolation
AWS Services
- Amazon SageMaker Overview - Documentation
- Amazon EFS Overview - Documentation
- Amazon Q Overview - Documentation
- AWS Identity and Access Management (IAM) Overview - Documentation
Module 2: Data Processing
Websites
- Feature Engineering at Scale with SageMaker Data Wrangler
- Prepare Data at Scale Using AWS Glue Interactive Sessions
- Amazon SageMaker Clarify
- Amazon SageMaker Pipelines
- What Is Amazon SageMaker?
- Amazon SageMaker Studio
- Amazon SageMaker Notebook Instances
- Import Data into SageMaker Data Wrangler from SaaS
- Import Data into SageMaker Data Wrangler from Amazon S3
- Interactive Data Prep Widget for Notebooks
- Data preparation using Amazon EMR
- Discover EMR Clusters in SageMaker Studio
- IPython Magics
- Configuring AWS Glue interactive sessions for Jupyter and AWS Glue Studio notebooks
- Choosing the Right Data Processing Option in SageMaker
- ImgAug Rotate Augmenter
- Transform Data with SageMaker Data Wrangler
AWS Services
- Amazon SageMaker Data Wrangler Overview - Documentation
- AWS Glue Overview - Documentation
- Amazon EMR Overview - Documentation
- Amazon S3 Overview - Documentation
Module 3: Model Development
Websites
- Bring Your Own Model with SageMaker Script Mode
- What’s New in AWS EC2 Trn1 Instances
- Amazon EC2 Instance Types
- SageMaker Built-in Algorithms
- Select ML Instances on the Fly in SageMaker Studio
- SageMaker Python SDK Documentation
- Docker Containers in SageMaker
- Tune R Bring Your Own Algorithm with HPO
- Choose the Best Data Source for Your SageMaker Training Job
- SageMaker Studio Image Build CLI
- SageMaker Training Toolkit
- Why Bring Your Own Container to SageMaker
- Amazon SageMaker Experiments
- Next-Generation SageMaker Experiments
- SageMaker Supports Chart Model Training Results
- View and Compare Experiments in SageMaker
- Adapt Your Own Docker Container for SageMaker
- Built-in Rules for SageMaker Debugger
- ML Explainability with SageMaker Debugger
- Automatic Model Tuning in SageMaker
- Choosing Algorithms in SageMaker
- Hyperparameter Tuning with Hyperband
- Early Stopping in Model Tuning
- Warm Start in Model Tuning
- Best Practices for Hyperparameter Tuning
- Resource Limits in SageMaker
- Training modes and algorithm support
- Explaining Autopilot Models with SHAP
- Detect Data Bias with SageMaker Clarify
- Amazon SageMaker JumpStart Simplifies Access to Prebuilt Models
- Hugging Face on SageMaker
- AWS and Hugging Face Collaboration
- Run Automatic Model Tuning with SageMaker JumpStart
- SageMaker Debugger
- Use Built-in Rules with SageMaker Debugger
Videos
- SageMaker Script Mode Tutorial
- EC2 Instance Types Explained
- Bring Your Own Model to SageMaker
- Build and Deploy ML Models with SageMaker
- SageMaker Containers and Training Toolkit
- ML Explainability with SageMaker Debugger
- Automatic Model Tuning in SageMaker
AWS Services
- Amazon SageMaker Overview - Documentation
- Amazon EC2 Overview - Documentation
- Amazon Q Overview - Documentation
- Amazon SageMaker Debugger Documentation
- Amazon SageMaker Experiments Documentation
- Amazon SageMaker Autopilot Documentation
Module 4: Deployment and Inference
Websites
- Deploy Models from the SageMaker Model Registry
- New ML Governance Tools for SageMaker
- Improve Governance of Your ML Models with SageMaker
- Build and Manage SageMaker Pipelines Steps
- View Pipeline Execution in SageMaker Studio
- SageMaker TensorFlow Serving with gRPC
- SageMaker Shadow Testing
- Amazon SageMaker Model Registry
Module 5: Monitoring
Websites
- Model Monitor Data Capture
- Schedule monitoring jobs
- Interpreting Violations in Model Monitor
- Model Quality Monitoring with SageMaker Model Monitor
- Learn How SageMaker Clarify Helps Detect Bias
- Fairness and Explainability with Model Monitor
- Feature Attribution Drift Detection
- SHAP Values in SageMaker Clarify
AWS Services
- Amazon SageMaker Model Monitor Documentation
- Amazon SageMaker Clarify Overview
Module 6: Managing SageMaker Studio Resources and Updates
Websites
- Amazon EFS Pricing
- Amazon SageMaker Pricing
- Analyze SageMaker Spend and Determine Cost Optimization Opportunities
- Save Costs by Automatically Shutting Down Idle Resources
- Shut Down SageMaker Notebook Instances
- SageMaker Studio Auto Shutdown Extension
- Create SageMaker Studio Lifecycle Configurations
- Automatically Shut Down SageMaker Studio Apps
- Shut Down and Update SageMaker Studio Classic and Studio Classic Apps
AWS Services
- Amazon EFS Overview - Documentation
- Amazon SageMaker Overview - Documentation
Module 7: Course Wrap-up
Websites
- Machine Learning (ML) and Artificial Intelligence (AI)
- Amazon SageMaker
- AWS Certification exam preparation
- AWS Certification
- AWS Recertification
- All AWS Certification exams
- Schedule an Exam
- AWS Skill Builder subscription
- AWS Training and Certification
- AWS Online Tech Talks
- AWS Ramp-Up Guides
Courses
- Advanced Architecting on AWS Online Course Supplement
- AWS Skill Builder
- AWS Builder Labs
- AWS Workshops