Learn to bring DevOps-style practices into the building, training, and deployment of ML models

Description
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. ML data platform engineers, DevOps engineers, and developers/operations staff with responsibility for operationalizing ML models will learn to address the challenges associated with handoffs 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 an MLOps action plan for your organization.

Who should take this course

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

Contents

• How to deploy your own models in the AWS Cloud
• How to automate workflows for building, training, testing, and deploying ML models
• The different deployment strategies for implementing ML models in production
• How to monitor for data drift and concept drift that could affect prediction and alignment with business expectations

I corsi di questa linea sono erogati in collaborazione con XPeppers (Claranet S.r.l.), in qualità di AWS Authorized Training Partner.

Durata

• 21 ore
• 3 giorni

Prerequisiti

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

Recommended

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