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Amazon SageMaker Studio for Data Scientists

  • Length 3 days
Course overview
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Why study this course

Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It does this by bringing together a broad set of capabilities purpose-built for ML.

This three-day, advanced level 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.

This course includes presentations, demonstrations, practice labs, discussions, and a capstone project.

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What you’ll learn

This course is designed to teach participants how to:

  • Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio

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AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

Who is the course for?

This course is intended for 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.

Course subjects

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, 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

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 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

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


  • 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

Please note: This is an emerging technology course. Course outline is subject to change as needed.


It is recommended that all attendees have the following prior to attending this course:

It is recommended that attendees who are not experienced data scientists also have the following prior to attending this course:

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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