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Practical Data Science with Amazon SageMaker

Cost
$675 USD
Course Code
AWS-PD-SAGE
Duration
1 Day
Format
Live Virtual Class

Course Schedule

Practical Data Science with Amazon SageMaker

$675.00

SCHEDULED, Aug.15.2024 - Aug.15.2024 ( 1 day), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Oct.15.2024 - Oct.15.2024 ( 1 day), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Jan.17.2025 - Jan.17.2025 ( 1 day), 09:00 AM - 05:00 PM US Pacific
SCHEDULED, Apr.29.2025 - Apr.29.2025 ( 1 day), 09:00 AM - 05:00 PM US Pacific
$675.00
$675.00
$675.00
$675.00
SKU: AWS-PD-SAGE Categories: , ,

Description

As artificial intelligence and machine learning (AI/ML) are quickly becoming part of our day-to-day, it is becoming increasingly more important to understand how to collaborate efficiently with data scientists and build applications that integrate with ML.

The Practical Science with Amazon SageMaker course will help you in your developer or DevOps engineer role understand the basics of ML and the steps involved in building ML models using Amazon SageMaker Studio. In this one-day, classroom training course an expert AWS instructor will walk you through how to prepare data and train, evaluate, tune, and deploy ML models.

Overview

What you’ll learn

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data and train, evaluate and tune, and deploy ML models
  • And much more

Audience

Who should take this course

  • Development operations (DevOps) engineers
  • Application developers

Suggested Prerequisites

What experience you’ll need

We recommend that attendees of this course have:

  • Taken the AWS Technical Essentials course
  • Basic understanding of Python programming
  • Basic knowledge of statistics

Topics

What you’ll learn

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data and train, evaluate and tune, and deploy ML models
  • And much more

Description

As artificial intelligence and machine learning (AI/ML) are quickly becoming part of our day-to-day, it is becoming increasingly more important to understand how to collaborate efficiently with data scientists and build applications that integrate with ML.

The Practical Science with Amazon SageMaker course will help you in your developer or DevOps engineer role understand the basics of ML and the steps involved in building ML models using Amazon SageMaker Studio. In this one-day, classroom training course an expert AWS instructor will walk you through how to prepare data and train, evaluate, tune, and deploy ML models.

Overview

What you’ll learn

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data and train, evaluate and tune, and deploy ML models
  • And much more

Audience

Who should take this course

  • Development operations (DevOps) engineers
  • Application developers

Suggested Prerequisites

What experience you’ll need

We recommend that attendees of this course have:

  • Taken the AWS Technical Essentials course
  • Basic understanding of Python programming
  • Basic knowledge of statistics

Topics

What you’ll learn

  • Discuss the benefits of different types of machine learning for solving business problems
  • Describe the typical processes, roles, and responsibilities on a team that builds and deploys ML systems
  • Explain how data scientists use AWS tools and ML to solve a common business problem
  • Summarize the steps a data scientist takes to prepare data and train, evaluate and tune, and deploy ML models
  • And much more

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