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MLOps Engineering on AWS

Course content updated by LearnQuest
Price
2,025 USD
3 Days
AWS-245
Classroom Training, Online Training
Amazon Web Services
Limited Time Offer
20% Off Check Point Training Courses
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Valid through October 31, 2022

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  • Date: 24-Oct-2022 to 26-Oct-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,025 USD
  • Date: 5-Dec-2022 to 7-Dec-2022
    Time: 9AM - 5PM US Eastern
    Location: Virtual
    Language: English
    Delivered by: LearnQuest
    Price: 2,025 USD
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Course Description

Overview

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

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.

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.

The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and
instructors.
  • Course level: Intermediate
  • Duration: 3 days, 8 hours/day

Objectives

What you'll learn
  • 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
  • And much more

Audience

Who should take this course
  • ML data platform engineers
  • DevOps engineers
  • Developers/operations staff with responsibility for operationalizing ML models

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

Topics

Describe machine learning operations
  • Understand the key differences between DevOps and MLOps
  • Describe the machine learning workflow
  • Discuss the importance of communications in MLOps
  • Explain end-to-end options for automation of ML workflows
  • List key Amazon SageMaker features for MLOps automation
  • Build an automated ML process that builds, trains, tests, and deploys models
  • Build an automated ML process that retrains the model based on change(s) to the model code
  • Identify elements and important steps in the deployment process
  • Describe items that might be included in a model package, and their use in training or inference
  • Recognize Amazon SageMaker options for selecting models for deployment, including support for
ML frameworks and built-in algorithms or bring-your-own-models
  • Differentiate scaling in machine learning from scaling in other applications
  • Determine when to use different approaches to inference
  • Discuss deployment strategies, benefits, challenges, and typical use cases
  • Describe the challenges when deploying machine learning to edge devices
  • Recognize important Amazon SageMaker features that are relevant to deployment and inference
  • Describe why monitoring is important
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