Amazon SageMaker Studio for Data Scientists [GK110001]

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Amazon SageMaker Studio for Data Scientists [GK110001]

Global Knowledge Network Training Ltd.
Logo Global Knowledge Network Training Ltd.
Provider rating: starstarstarstarstar_border 7.5 Global Knowledge Network Training Ltd. has an average rating of 7.5 (out of 2 reviews)

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Starting dates and places

computer Online: VIRTUAL TRAINING CENTER
20 Aug 2024 until 22 Aug 2024
computer Online: VIRTUAL TRAINING CENTER
30 Sep 2024 until 2 Oct 2024
place(Virtual Training Centre)
26 Nov 2024 until 28 Nov 2024
place(Virtual Training Centre)
17 Feb 2025 until 19 Feb 2025
place(Virtual Training Centre)
1 Apr 2025 until 20 Aug 2025
place(Virtual Training Centre)
19 May 2025 until 21 May 2025
computer Online: VIRTUAL TRAINING CENTER
18 Aug 2025 until 20 Aug 2025
computer Online: VIRTUAL TRAINING CENTER
13 Oct 2025 until 15 Oct 2025
place(Virtual Training Centre)
24 Nov 2025 until 26 Nov 2025

Description

OVERVIEW

Explore Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models.

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.

OBJECTIVES

In this course, you will learn to:

  • Accelerate the process to prepare, build, train, deploy, and monito…

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Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

OVERVIEW

Explore Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models.

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.

OBJECTIVES

In this course, you will learn to:

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

AUDIENCE

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

CONTENT

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
  • Using AWS Glue interactive sessions
  • Using SageMaker Processing with custom scripts

Module 3: Model Development

  • SageMaker training jobs
  • Built-in algorithms
  • Bring your own script
  • Bring your own container
  • SageMaker Experiments


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
  • SageMaker Jumpstart

Module 4: Deployment and Inference

  • SageMaker Model Registry
  • SageMaker Pipelines
  • SageMaker model inference options
  • Scaling
  • Testing strategies, performance, and optimization

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

Lab Outlines

  • 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
  • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
  • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models
  • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
  • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
  • Hands-On Lab: Inferencing with SageMaker Studio

 

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    There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.