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The Machine Learning Pipeline on AWS

Cost
$2700 USD
Course Code
AWS-ML-PIPE
Duration
4 Days
Format
Live Virtual Class

Course Schedule

The Machine Learning Pipeline on AWS

$2,700.00

SCHEDULED, Oct.15.2024 - Oct.18.2024 ( 4 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Jan.14.2025 - Jan.17.2025 (4 days), 09:00 AM - 05:00 PM US Pacific
SCHEDULED, Aug.19.2024 - Aug.22.2024 (4 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Apr.08.2025 - Apr.11.2025 ( 4 days), 09:00 AM - 05:00 PM US Eastern
$2,700.00
$2,700.00
$2,700.00
$2,700.00
SKU: AWS-ML-PIPE Categories: , ,

Description

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays.

Overview

Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

Audience

Who should take this course

  • Developers
  • Solutions architects
  • Data engineers
  • Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Suggested Prerequisites

What experience you’ll need

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment

Topics

What you’ll learn

  • Select and justify the appropriate ML approach for a given business problem
  • Use the ML pipeline to solve a specific business problem
  • Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS

Description

This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays.

Overview

Students will learn about each phase of the pipeline from instructor presentations and demonstrations. They will then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.

Audience

Who should take this course

  • Developers
  • Solutions architects
  • Data engineers
  • Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Suggested Prerequisites

What experience you’ll need

  • Basic knowledge of Python programming language
  • Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
  • Basic experience working in a Jupyter notebook environment

Topics

What you’ll learn

  • Select and justify the appropriate ML approach for a given business problem
  • Use the ML pipeline to solve a specific business problem
  • Train, evaluate, deploy, and tune an ML model in Amazon SageMaker
  • Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS

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