IT-Trainer Jobs und Stellenangebote: Amazon SageMaker Studio for Data Scientists

Seminardauer: 3 Tage

Trainer gesucht

IT-Trainer Jobs und Stellenangebote: Amazon SageMaker Studio for Data Scientists, Amazon SageMaker, Amazon Web Services (AWS), Data Science, Data Scientist.

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Agenda

Amazon SageMaker Studio Setup

  • JupyterLab Extensions in SageMaker Studio
  • Demonstration: SageMaker user interface demo

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

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
  • 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

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
  • Amazon SageMaker Studio for Data Scientists
  • Scaling
  • Testing strategies, performance, and optimization
  • Hands-On Lab: Inferencing with SageMaker Studio

Monitoring

  • Amazon SageMaker Model Monitor
  • Discussion: Case study
  • Demonstration: Model Monitoring

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