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AWS-SMS-DS

Amazon SageMaker Studio for Data Scientists Training

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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Course Details

Duration

3 days

Prerequisites

  • Complete AWS Technical Essentials course or have equivalent experience.
  • Individuals who are not experienced data scientists should complete The Machine Learning Pipeline on AWS and Deep Learning on AWS courses then
  • Have 1-year on-the-job experience building models

Target Audience

  • 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.

Skills Gained

Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio.
Course Outline
  • Amazon SageMaker Setup and Navigation
    • Launch SageMaker Studio from the AWS Service Catalog.
    • Navigate the SageMaker Studio UI.
  • 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.
  • 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
  • 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.
  • 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.
  • 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