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MLOps Engineering on AWS

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

Course Schedule

MLOps Engineering on AWS

$2,025.00

SCHEDULED, Aug.21.2024 - Aug.23.2024 ( 3 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Oct.30.2024 - Nov.01.2024 ( 3 days), 09:00 AM - 05:00 PM US Eastern
SCHEDULED, Jan.29.2025 - Jan.31.2025 ( 3 days), 09:00 AM - 05:00 PM US Pacific
SCHEDULED, Apr.09.2025 - Apr.11.2025 ( 3 days), 09:00 AM - 05:00 PM US Eastern
$2,025.00
$2,025.00
$2,025.00
$2,025.00
SKU: AWS-MLOps Categories: , ,

Description

In this 3-day, course you will learn how to address the challenges associated with hand-offs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building and troubleshooting an ML pipeline.

Overview

Could your Machine Learning (ML) workflow use some DevOps agility? MLOps Engineering on AWS will help you bring DevOps-style practices into the building, training, and deployment of ML models by learning from an expert AWS instructor.

In this 3-day, course you will learn how to address the challenges associated with hand-offs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building and troubleshooting an ML pipeline.

Audience

Who should take this course

  • MLOps engineers who want to productionize and monitor ML models in the AWS cloud
  • DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production

Suggested Prerequisites

What experience you’ll need

Required:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience

Topics

What you’ll learn

  • Explain the benefits of MLOps
  • Compare and contrast DevOps and MLOps
  • Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
  • Set up experimentation for MLOps with Amazon SageMaker
  • And much more

Description

In this 3-day, course you will learn how to address the challenges associated with hand-offs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building and troubleshooting an ML pipeline.

Overview

Could your Machine Learning (ML) workflow use some DevOps agility? MLOps Engineering on AWS will help you bring DevOps-style practices into the building, training, and deployment of ML models by learning from an expert AWS instructor.

In this 3-day, course you will learn how to address the challenges associated with hand-offs between data engineers, data scientists, software developers, and operations through the use of tools, automation, processes, and teamwork. By the end of the course, go from learning to doing by building and troubleshooting an ML pipeline.

Audience

Who should take this course

  • MLOps engineers who want to productionize and monitor ML models in the AWS cloud
  • DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production

Suggested Prerequisites

What experience you’ll need

Required:

  • AWS Technical Essentials course (classroom or digital)
  • DevOps Engineering on AWS course, or equivalent experience
  • Practical Data Science with Amazon SageMaker course, or equivalent experience

Topics

What you’ll learn

  • Explain the benefits of MLOps
  • Compare and contrast DevOps and MLOps
  • Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
  • Set up experimentation for MLOps with Amazon SageMaker
  • And much more

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