Cloud Computing and Virtualisation

Amazon SageMaker Studio for Data Scientists

  • Length 3 days
  • Price  $2860 inc GST
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


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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 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.


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