PARTNER SageMaker Studio for Data Scientists Installation Guide

: June 15, 2024
: PARTNER

Table of Contents

PARTNER SageMaker Studio for Data Scientists
Product Information
Course Subjects
WHO IS THE COURSE FOR?
PREREQUISITES
FAQ’s
References
Read User Manual Online (PDF format)
Download This Manual (PDF format)

PARTNER SageMaker Studio for Data Scientists

PARTNER-SageMaker-Studio-for-Data-Scientists-product

Product Information

Product Name: Amazon SageMaker Studio for Data Scientists
Length: 3 days
Price (Incl. GST): $2860
Provider: Lumify Work
Official AWS Training Partner: Australia, New Zealand, and the Philippines
Description: Amazon SageMaker Studio is a comprehensive platform designed specifically for data scientists to prepare, build, train, deploy, and monitor machine learning (ML) models quickly. It offers a wide range of capabilities that are purpose-built for ML tasks.

CLOUD COMPUTING AND VIRTUALISATION
Amazon SageMaker Studio for Data Scientists

LENGTH: 3 days
PRICE (Incl. GST): $2860

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.

Why Study This Course
This three-day advanced level course is designed to help experienced data scientists improve their productivity throughout the entire ML lifecycle using the tools available in SageMaker Studio. The course includes presentations, demonstrations, practice labs, discussions, and a capstone project.

What You’ll Learn
This course will teach participants how to:

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

Course Subjects

Module 1: Amazon SageMaker Set up 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, visualize, analyze, 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: Analyze and Prepare Data Using SageMaker Data Wrangler
Lab 3: Analyze 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

My instructor was great being able to put scenarios into real world instances that related to my specific situation. I was made to feel welcome from the moment I arrived and the ability to sit as a group outside the classroom to discuss our situations and our goals was extremely valuable. I learnt a lot and felt it was important that my goals by attending this course were met. Great job Lumify Work team.

AMANDA NICOL
IT SUPPORT SERVICES MANAGER – HEALT H WORLD LIMIT ED

Module 4: Deployment and Inf erence

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

Lumify Work Customised Training
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 1 800 853 276.

Module 5: Monit oring

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 St udio Resources and Updat es

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

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

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.

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 organisat ion t ime, money and resources. For more inf ormat ion, please cont act us on 1800 U LEARN (1800 853 276)

PREREQUISITES

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

Complet ed AWS Technical Essent ials course
Experience in Pyt hon programming

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

Completed The Machine Learning Pipeline on AWS course
Completed Deep Learning on AWS course

The s upply of this cours e by Lumify Work is governed by the booking terms and conditions . Pleas e read the terms and conditions carefully before enrolling in this cours e, as enrolment in the cours e is conditional on acceptance of thes e terms and conditions .

https://www.lumifywork.com/en-au/courses/amazon-sagemaker-studio-for-data- scientists/

Call 1800 853 276 and speak to a Lumify Work Consultant today!

FAQ’s

How long is the course?

The course is 3 days long.

What is the price of the course?

The price of the course, including GST, is $2860.

Who is the provider of this course?

Lumify Work is the official AWS Training Partner for Australia, New Zealand, and the Philippines.

What is Amazon SageMaker Studio?

Amazon SageMaker Studio is a comprehensive platform designed specifically for data scientists to prepare, build, train, deploy, and monitor machine learning (ML) models quickly.