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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


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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:

Recommended:


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.

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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.


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