FORGOT YOUR DETAILS?

eluide9eb7f5d

MLOps Engineering on AWS

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

Course Schedule

MLOps Engineering on AWS

$2,025.00

Scheduled, 02-Aug-2022 - 04-Aug-2022, 09:00 AM - 05:00 PM US Eastern
Scheduled, 06-Sep-2022 - 08-Sep-2022, 09:00 AM - 05:00 PM US Pacific
Scheduled, 04-Oct-2022 - 06-Oct-2022, 09:00 AM - 05:00 PM US Eastern
Scheduled, 01-Nov-2022 - 03-Nov-2022, 09:00 AM - 05:00 PM US Eastern
Choose an option
MLOps Engineering on AWS – Scheduled, 02-Aug-2022 - 04-Aug-2022, 09:00 AM - 05:00 PM US Eastern
$2,025.00
MLOps Engineering on AWS – Scheduled, 06-Sep-2022 - 08-Sep-2022, 09:00 AM - 05:00 PM US Pacific
$2,025.00
MLOps Engineering on AWS – Scheduled, 04-Oct-2022 - 06-Oct-2022, 09:00 AM - 05:00 PM US Eastern
$2,025.00
MLOps Engineering on AWS – Scheduled, 01-Nov-2022 - 03-Nov-2022, 09:00 AM - 05:00 PM US Eastern
$2,025.00
Clear
SKU: AWS-MLO-ENG Categories: ,

Overview

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.

Audience

  • DevOps engineers
  • ML engineers
  • Developers/operations with responsibility for operationalizing ML models

Suggested Prerequisites

  • 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
  • Recommended: The Elements of Data Science (digital course), or equivalent experience
  • Recommended: Machine Learning Terminology and Process (digital course)

Topics

  • Introduction to MLOps
  • MLOps Development
  • MLOps Deployment
  • Model Monitoring and Operations
  • Wrap-up

Overview

This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and code to successful ML deployments. It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.

Audience

  • DevOps engineers
  • ML engineers
  • Developers/operations with responsibility for operationalizing ML models

Suggested Prerequisites

  • 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
  • Recommended: The Elements of Data Science (digital course), or equivalent experience
  • Recommended: Machine Learning Terminology and Process (digital course)

Topics

  • Introduction to MLOps
  • MLOps Development
  • MLOps Deployment
  • Model Monitoring and Operations
  • Wrap-up
TOP
0