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Amazon SageMaker Studio for Data Scientists

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle

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3
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Deutsch
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Was werden Sie in diesem Training erlernen?

In this course, you will learn to: Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio

Agenda

Amazon SageMaker Setup and Navigation
Launch SageMaker Studio from the AWS Service Catalog.
Navigate the SageMaker Studio UI
Demo 1: SageMaker UI Walkthrough
Lab 1: Launch SageMaker Studio from AWS Service Catalog
Data Processing
Use Amazon SageMaker Studio to collect, clean, visualize, analyze, and transform data.
Set up a repeatable process for data processing.
Use SageMaker to validate that collected data is ML ready.
Detect bias in collected data and estimate baseline model accuracy
Lab 2: Analyze and Prepare Data Using SageMaker Data Wrangler
Lab 3: Analyze and Prepare Data at Scale Using Amazon EMR
Lab 4: Data Processing Using SageMaker Processing and the SageMaker Python SDK
Lab 5: Feature Engineering Using SageMaker Feature Store
Model Development
Use Amazon SageMaker Studio to develop, tune, and evaluate an ML model against business objectives and fairness and explainability best practices
Fine-tune ML models using automatic hyperparameter optimization capability
Use SageMaker Debugger to surface issues during model development.
Demo 2: Autopilot
Lab 6: Track Iterations of Training and Tuning Models Using SageMaker Experiments
Lab 7: Analyze, Detect, and Set Alerts Using SageMaker Debugger
Lab 8: Identify Bias Using SageMaker Clarify
Deployment and Inference
Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a mode
Design and implement a deployment solution that meets inference use case requirements
Create, automate, and manage end-to-end ML workflows using Amazon SageMaker Pipelines
Lab 9: Inferencing with SageMaker Studio
Lab 10: Using SageMaker Pipelines and the SageMaker Model Registry with SageMaker Studio
Monitoring
Configure a SageMaker Model Monitor solution to detect issues and initiate alerts for changes in data quality, model quality, bias drift, and feature attribution (explainability) drift
Create a monitoring schedule with a predefined interval.
Demo 3: Model Monitoring
Managing SageMaker Studio Resources and Updates
List resources that accrue charges
Recall when to shut down instances
Explain how to shut down instances, notebooks, terminals, and kernels
Understand the process to update SageMaker Studio.
Capstone
The Capstone lab will bring together the various capabilities of SageMaker Studio discussed in this course. Students will be given the opportunity to prepare, build, train, and deploy a model using a tabular dataset not seen in earlier labs. Students can choose among basic, intermediate, and advanced versions of the instructions.
Capstone Lab: Build an End-to-End Tabular Data ML Project Using SageMaker Studio and the SageMaker Python SDK
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications
  • Group Exercise: Review Architecting on AWS core best practices
  • AWS Organizations for multi-account access and permissions
  • AWS SSO to simplify access and authentication across AWS accounts and third-party services
  • AWS Control Tower
  • Permissions, access, and authentication
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications
  • Group Exercise: Review Architecting on AWS core best practices
  • Lab 1: Securing Amazon S3 VPC Endpoint Communications
Experienced data scientists who are proficient in ML and deep learning fundamentals. Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
AWS Tech Essentials (1–day AWS instructor led course)
AWS

795,00 €

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