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MLOps Engineering on AWS

Length 3 days

Course overview

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Why study this course

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 organisation through daily reflection of lesson and lab content, and through conversations with peers and instructors.

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What you’ll learn

This course is designed to teach participants how to:

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
Recognise 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
Recognise 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

AWS at Lumify Work

Lumify Work is an official AWS Training Partner for Australia, New Zealand, and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path that’s relevant to you and your organisation, so you can get more out of the cloud. We offer virtual and face-to-face classroom-based training to help you build your cloud skills and enable you to achieve industry-recognised AWS Certification.

Stay ahead of the technology curve

Don’t let your tech outpace the skills of your people

Quality Instructors and Content

Expert instructors with real world experience and the latest vendor-approved in-depth course content.

Partner-Preferred Supplier

Chosen and awarded by the world's leading vendors as preferred training partner.

Ahead of the Technology Curve

No matter your chosen technologies or platforms, we can help you stay one step ahead.

Who is the course for?

This course is intended for any one of the following roles with responsibility for productionising machine learning models in the AWS Cloud:

DevOps engineers
ML engineers
Developers/operations with responsibility for operationalising ML models

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please contact us on 1800 U LEARN (1800 853 276)

Course subjects

Module 0: Welcome

Course introduction

Module 1: Introduction to MLOps

Machine learning operations
Goals of MLOps
Communication
From DevOps to MLOps
ML workflow
Scope
MLOps view of ML workflow
MLOps cases

Module 2: MLOps Development

Intro to build, train, and evaluate machine learning models
MLOps security
Automating
Apache Airflow
Kubernetes integration for MLOps
Amazon SageMaker for MLOps
Lab: Bring your own algorithm to an MLOps pipeline
Demonstration: Amazon SageMaker
Intro to build, train, and evaluate machine learning models
Lab: Code and serve your ML model with AWS CodeBuild
Activity: MLOps Action Plan Workbook

Module 3: MLOps Deployment

Introduction to deployment operations
Model packaging
Inference
Lab: Deploy your model to production
SageMaker production variants
Deployment strategies
Deploying to the edge
Lab: Conduct A/B testing
Activity: MLOps Action Plan Workbook

Module 4: Model Monitoring and Operations

Lab: Troubleshoot your pipeline
The importance of monitoring
Monitoring by design
Lab: Monitor your ML model
Human-in-the-loop
Amazon SageMaker Model Monitor
Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature
Store
Solving the Problem(s)
Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

Course review
Activity: MLOps Action Plan Workbook
Wrap-up

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Prerequisites

Required:

AWS Technical Essentials
DevOps Engineering on AWS or equivalent experience
Practical Data Science with Amazon SageMaker or equivalent experience

Recommended:

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

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

Request Course Information

By submitting an enquiry, you agree to our privacy policy and receiving email and other forms of communication from us. You can opt-out at any time.