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
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.
Stay ahead of the technology curve
Don’t let your tech outpace the skills of your people
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
Quality Instructors and Content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world's leading vendors as preferred training partner.
Ahead of the Technology Curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
Quality Instructors and Content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world's leading vendors as preferred training partner.
Ahead of the Technology Curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
We have a 30-year history of driving innovative, award-winning learning solutions.
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations, and from more vendors.
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
We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please contact us on 1800 U LEARN (1800 853 276)
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
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
Please note: This is an emerging technology course. Course outline is subject to change as needed.
Prerequisites
It is recommended that all attendees have the following prior to attending this course:
Access interactive training developed by the experts at AWS.
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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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By submitting an enquiry, you agree to our privacy policy and receiving email and other forms of communication from us. You can opt-out at any time.