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MLOps Engineering on AWS

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.

Course Overview

Overall Proficiency Level
2 - Intermediate
Course Catalog Number
MLOPSEAWS
Course Prerequisites

Good understanding of DevOps and AWS architecture.

Training Purpose
Skill Development
Specific Audience
Academia
Contractor
Federal Employee
Delivery Method
Classroom
Online, Instructor-Led
Course Location

4176 S Plaza Trail
Suite 207
Virginia Beach, VA 23452

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Learning Objectives

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 Detect data drifts in the underlying input data Demonstrate how to monitor ML models for bias Explain how to monitor model resource consumption and latency Discuss how to integrate human-in-the-loop reviews of model results in production

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Feedback

If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.

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