Amazon SageMaker Studio for Data Scientists (AW-ASSDS)

Course 1885

  • Duration: 3 days
  • Language: English
  • Level: Advanced

Amazon SageMakerStudio 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.

SageMaker Studio Training Delivery Methods

  • In-Person

  • Online

SageMaker Studio Training Information

In this course, you will learn how to:

Accelerate the preparation, building, training, deployment, and monitoring of ML (Machine Learning) solutions by using Amazon SageMaker Studio.

Training Prerequisites

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

SageMaker Studio Training Outline

  • Launch SageMaker Studio from the AWS Service Catalogue.
  • Navigate the SageMaker Studio UI.

Demo 1: SageMaker UI Walkthrough

Lab 1: Launch SageMaker Studio from AWS Service Catalogue

  • Use Amazon SageMaker Studio to collect, clean, visualise, analyse, 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: Analyse and Prepare Data Using SageMaker Data Wrangler

Lab 3: Analyse 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

  • 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 optimisation 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: Analyse, Detect, and Set Alerts Using SageMaker Debugger

Lab 8: Identify Bias Using SageMaker Clarify

  • 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

  • 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

  • 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.
  • 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

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SageMaker Studio Training FAQs

Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.

For a list of the supported Amazon SageMaker AWS Regions, please visit the AWS Regional Services page. Also, for more information, see Regional endpoints in the AWS general reference guide.

Amazon SageMaker is designed for high availability. There are no maintenance windows or scheduled downtimes. SageMaker APIs run in Amazon’s proven, high-availability data centers, with service stack replication configured across three facilities in each AWS Region to provide fault tolerance in the event of a server failure or Availability Zone outage.

Amazon SageMaker stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.

Amazon SageMaker ensures that ML model artefacts and other system artefacts are encrypted in transit and at rest. Requests to the SageMaker API and console are made over a secure (SSL) connection. You pass AWS Identity and Access Management roles to SageMaker to provide permissions to access resources on your behalf for training and deployment. You can use encrypted Amazon Simple Storage Service (Amazon S3) buckets for model artefacts and data, as well as pass an AWS Key Management Service (KMS) key to SageMaker notebooks, training jobs, and endpoints to encrypt the attached ML storage volume. Amazon SageMaker also supports Amazon Virtual Private Cloud (VPC) and AWS PrivateLink support.

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.

This training is available both in-person and online.

We recommend that all students complete the AWS Tech Essentials course prior to attending this training. For those who are not experienced data scientists, we recommend completing two courses (The Machine Learning Pipeline on AWS and Deep Learning on AWS) and 1-year on-the-job experience building models prior to taking this course.

The course covers the following topics:

  • SageMaker Studio setup and navigation
  • Data processing
  • Model development
  • Deployment and Inference
  • Monitoring
  • Managing SageMaker Studio resources and updates
  • Capstone lab to build an end-to-end ML project using SageMaker Studio