Amazon SageMaker Studio for Data Scientists - Online Training - Webinar von tecRacer Consulting GmbH

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machinelearning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built forML. This course prepares experienced data scientists to use the tools that are a part of SageMakerStudio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improveproductivity at every step of the ML lifecycle.

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 SageMakerPython 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 TuningModels

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 SageMakerStudio• 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 modeloptimization• 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