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Amazon SageMaker Studio for Data Scientists

WGAC-AWS-ASSDS

Amazon Web Services Training Courses Certification

Schedule

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Description

Module 1: 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
Module 2: 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
Module 3: 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
Module 4: Deployment and Inference
  • Use Model Registry to create a model group; register, view, and manage model versions; modify model approval status; and deploy a model.
  • 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
Module 5: 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
Module 6: 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

We recommend that all students complete the following AWS course prior to attending this course:

We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:

  • 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

  • Amazon SageMaker Setup and Navigation
  • Data Processing
  • Model Development
  • Deployment and Inference
  • Monitoring
  • Managing SageMaker Studio Resources and Updates
  • Capstone
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