Amazon SageMaker Studio for Data Scientists

Code: MLASMS

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.

  • Course level: Advanced
  • Duration: 3 days


Activities

This course includes presentations, hands-on labs, demonstrations, discussions, and a capstone project.


Course Objectives

In this course, you will learn to:

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


Intended Audience

This course is intended for:

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


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
  • AWS Technical Essentials digital or classroom training 


Course Outline

Day 1

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


Day 2

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


Day 3

Module 6: Managing SageMaker Studio Resources and Updates

  • Accrued cost and shutting down
  • Updates

Capstone

  • 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

Price (ex. VAT)

€ 3.284,00 per person

Duration

3 days

Schedule

Please send us a message with the form below

Delivery methods

  • Classroom
  • On-site (at your location)
  • Virtual (instructor online)

Inquire

We will contact you to discuss your requirements