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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, hands-on labs, demonstrations, discussions, and a capstone project.

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What you’ll learn

This course is designed to teach participants how to:

Accelerate the process to prepare, build, train, deploy, and monitor ML solutions using Amazon SageMaker Studio

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.

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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 Studio Setup

JupyterLab Extensions in SageMaker Studio
Demonstration: SageMaker user interface demo

Module 2: Data Processing

Using SageMaker Data Wrangler for data processing
Hands-On Lab: Analyse and prepare data using Amazon SageMaker Data Wrangler
Using Amazon EMR
Hands-On Lab: Analyse and prepare data at scale using Amazon EMR
Using AWS Glue interactive sessions
Using SageMaker Processing with custom scripts
Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
SageMaker Feature Store
Hands-On Lab: Feature engineering using SageMaker Feature Store

Module 3: Model Development

SageMaker training jobs
Built-in algorithms
Bring your own script
Bring your own container
SageMaker Experiments
Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

Module 3: Model Development (continued)

SageMaker Debugger
Hands-On Lab: Analysing, Detecting, and Setting Alerts Using SageMaker Debugger
Automatic model tuning
SageMaker Autopilot: Automated ML
Demonstration: SageMaker Autopilot
Bias detection
Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
SageMaker Jumpstart

Module 4: Deployment and Inference

SageMaker Model Registry
SageMaker Pipelines
Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
SageMaker model inference options
Scaling
Testing strategies, performance, and optimisation
Hands-On Lab: Inferencing with SageMaker Studio

Module 5: Monitoring

Amazon SageMaker Model Monitor
Discussion: Case study
Demonstration: Model Monitoring

Module 6: Managing SageMaker Studio Resources and Updates

Accrued cost and shutting down
Updates

Capstone

Environment setup
Challenge 1: Analyse and prepare the dataset with SageMaker Data Wrangler
Challenge 2: Create feature groups in SageMaker Feature Store
Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
(Optional) Challenge 4: Use SageMaker Debugger for training performance and model optimisation
Challenge 5: Evaluate the model for bias using SageMaker Clarify
Challenge 6: Perform batch predictions using model endpoint
(Optional) Challenge 7: Automate full model development process using SageMaker Pipelines

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

Prerequisites

We recommend that all attendees of this course have:

Experience using ML frameworks
Python programming experience
At least 1 year of experience as a data scientist responsible for training, tuning, and deploying models
Completed AWS Technical Essentials 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.