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Amazon SageMaker Studio for Data Scientists

Cost
$2025 USD
Course Code
AWS-SAGE-DS
Duration
3 Days
Format
Live Virtual Class

Course Schedule

Amazon SageMaker Studio for Data Scientists

$2,025.00

SCHEDULED, Aug.14.2024 - Aug.16.2024 ( 3 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Oct.22.2024 - Oct.24.2024 (3 days), 09:00 AM - 05:00 PM US Pacific
SCHEDULED, Jan.21.2025 - Jan.23.2025 ( 3 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Apr.15.2025 - Apr.17.2025 ( 3 days), 09:00 AM - 05:00 PM US Eastern
$2,025.00
$2,025.00
$2,025.00
$2,025.00
SKU: AWS-SAGE-DS Categories: , ,

Description

Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.

Overview

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. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

Audience

Who should take this course

  • 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.

Suggested Prerequisites

What experience you’ll need

We recommend that all students complete the following AWS course prior to attending this course:

  • AWS Technical Essentials (1–day AWS instructor led course)

We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:

  • The Machine Learning Pipeline on AWS (4–day AWS instructor led course)

Topics

What you’ll learn

  • Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
  • Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
  • And much more

Description

Learn to boost productivity at every step of the ML lifecycle with Amazon SageMaker Studio for Data Scientists from an expert AWS instructor.

Overview

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. This course includes presentations, demonstrations, discussions, labs, and at the end of the course, you’ll practice building an end-to-end tabular data ML project using SageMaker Studio and the SageMaker Python SDK.

Audience

Who should take this course

  • 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.

Suggested Prerequisites

What experience you’ll need

We recommend that all students complete the following AWS course prior to attending this course:

  • AWS Technical Essentials (1–day AWS instructor led course)

We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:

  • The Machine Learning Pipeline on AWS (4–day AWS instructor led course)

Topics

What you’ll learn

  • Accelerate the preparation, building, training, deployment, and monitoring of ML solutions by using Amazon SageMaker Studio
  • Use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle
  • And much more

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