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Python ProgrammingAmazon SageMaker Studio for Data Scientists
Learn to use Amazon SageMaker Studio to boost productivity at every step of the ML lifecycle
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Course : Amazon SageMaker Studio for Data Scientists
Level : Advanced
Type : Live online or Classroom
Length :3 days
This course includes presentations, demonstrations, discussions, labs, and at the end of the course, youβll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.
Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.
Experienced data scientists who are proficient in ML and deep learning fundamentals.
Basic understanding of Python programming
Basic knowledge of MLOps
Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
And much more
Instructor-led Virtual online live Training.
Weekend Classes
Evening Classes
Weekdays Classes
Course Outline
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
Using Amazon EMR
Using AWS Glue interactive sessions
Using SageMaker Processing with custom scripts
Python SDK
SageMaker Feature Store
Hands-on Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
Hands-on Lab: Analyze and prepare data at scale using Amazon EMR
Hands-on Lab: Data processing using Amazon SageMaker Processing and SageMaker
Module 3: Model Development
SageMaker training jobs
Built-in algorithms
Bring your own script
Bring your own container
SageMaker Experiments
SageMaker Debugger
Automatic model tuning
SageMaker Autopilot: Automated ML
Demonstration: SageMaker Autopilot
Bias detection
SageMaker Jumpstart
Hands-on Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
Hands-on Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
Hands-on Lab: Using SageMaker Clarify for Bias and Explainability
Module 4: Deployment and Inference
SageMaker Model Registry
SageMaker Pipelines
SageMaker model inference options
Scaling
Testing strategies, performance, and optimization
Hands-on Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
Hands-on Lab: Inferencing with SageMaker Studio
Module 5: Monitoring
Amazon SageMaker Model Monitor
Discussion: Case study
Demonstration: Model Monitoring
Module 6: Managing SageMaker Studio Resources and Updates
Accrued cost and shutting down
Updates
Hands-on Lab: Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
Hands-on Lab: Challenge 2: Create feature groups in SageMaker Feature Store
Hands-on Lab: Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
Hands-on Lab: Challenge 4: Use SageMaker Debugger for training performance and model optimization
Hands-on Lab: Challenge 5: Evaluate the model for bias using SageMaker Clarify
Hands-on Lab: Challenge 6: Perform batch predictions using model endpoint
Hands-on Lab: Challenge 7: Automate full model development process using SageMaker Pipeline
Best Value offer
Official Courseware
Instructor-led live online / Classroom
Flexible Schedule
Course Completion Certification
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