Amazon SageMaker Studio for Data Scientists Training

Live Online & Classroom Enterprise Certification Training

Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor. The three-day, advanced level course helps experienced data scientists build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows to reduce training time from hours to minutes with optimized infrastructure.

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What is Amazon SageMaker Studio for Data Scientists Certification Training about?

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 course prepares experienced data scientists to use the tools that are a part of SageMaker Studio, including Amazon CodeWhisperer and Amazon CodeGuru Security scan extensions, to improve productivity at every step of the ML lifecycle.

What are the objectives of Amazon SageMaker Studio for Data Scientists Certification Training ?

In this course, you will learn to:

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

Who is Amazon SageMaker Studio for Data Scientists Certification Training for?

This course is intended for:

Experienced data scientists who are proficient in ML and deep learning fundamentals

What are the prerequisites for Amazon SageMaker Studio for Data Scientists Certification Training?

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

Available Training Modes

Live Online Training

3 Days

Course Outline Expand All

Expand All

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: Analyze and prepare data using Amazon SageMaker Data Wrangler

Using Amazon EMR

Hands-On Lab: Analyze 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: Analyzing, 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 optimization

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

UpdatesCapstone

Environment setup

Challenge 1: Analyze 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 optimization

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 Pipeline

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Who is the instructor for this training?

The trainer for this Amazon SageMaker Studio for Data Scientists Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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