GB
/
GBP
/
EN_GB

Shaping the future of IT skills

Maximising IT performance through learning

MLOps Engineering on AWS - MLOE

WGAC-AWS-MLOE

Amazon Web Services

Description

Show Tabs
Introduction
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.
Prerequisites & Audience

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)
Course Benefits

In this course, you will learn 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
  • 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
Course Topics

Day 1 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

Day 2 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

Day 3 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

Amazon Web Services courses


AWS Discovery Day (3 hours) - AWSDD3H
CODE: WGAC-AWS-AWSDD3H
Machine Learning Pipeline on AWS - ML-PIPE
CODE: WGAC-AWS-ML-PIPE
Exam Readiness: AWS Certified Database – Specialty - ACDS-EX
CODE: WGAC-AWS-ACDS-EX
Exam Readiness: AWS Certified Data Analytics – Specialty - ACDAS-EX
CODE: WGAC-AWS-ACDAS-EX
Migrating to AWS - AWSM
CODE: WGAC-AWS-AWSM
Exam Readiness Intensive Workshop: AWS Certified Solutions Architect – Associate - ACSAA-EXIW
CODE: WGAC-AWS-ACSAA-EXIW
Deep Learning on AWS - AWSDL
CODE: WGAC-CSC-AWSDL
Exam Readiness: AWS Certified Developer - Associate - ACDA-EX
CODE: WGAC-AWS-ACDA-EX
Advanced Architecting on AWS - AAAWS
CODE: WGAC-AWS-AAAWS
Building Data Lakes on AWS - BDLA
CODE: WGAC-AWS-BDLA
AWS Cloud Ready Hackathon: Running Cloud Workloads with Kubernetes - AWSHRWK
CODE: WGAC-AWS-AWSHRWK
Building Data Analytics Solutions Using Amazon Redshift - BDASAR
CODE: WGAC-AWS-BDASAR
AWS Security Essentials - SEC-ESS
CODE: WGAC-AWS-SEC-ESS
Developing on AWS - AWSD
CODE: WGAC-AWS-AWSD
MLOps Engineering on AWS - MLOE
CODE: WGAC-AWS-MLOE
AWS Technical Essentials - AWSE
CODE: WGAC-AWS-AWSE
Advanced Developing on AWS - ADV-DEV
CODE: WGAC-AWS-ADV-DEV
Practical Data Science with Amazon SageMaker - PDSASM
CODE: WGAC-AWS-PDSASM
Exam Readiness: AWS Certified Solutions Architect – Professional - ACSAP-EX
CODE: WGAC-AWS-ACSAP-EX
AWS Cloud Essentials for Business Leaders – Financial Services - CEBL-FS
CODE: WGAC-AWS-CEBL-FS
DevOps Engineering on AWS - AWSDEVOPS
CODE: WGAC-AWS-AWSDEVOPS
Exam Readiness: AWS Certified Solutions Architect – Associate - ACSAA-EX
CODE: WGAC-AWS-ACSAA-EX
AWS Cloud Ready Hackathon: Coding and Testing on Linux - AWSHCTL
CODE: WGAC-AWS-AWSHCTL
Big Data on AWS - BDAWS
CODE: WGAC-AWS-BDAWS
AWS Security Governance at Scale - SGS
CODE: WGAC-AWS-SGS
AWS Cloud Ready Hackathon: Containers, Kubernetes CI & CD - AWSHCKC
CODE: WGAC-AWS-AWSHCKC
AWS Cloud Essentials for Business Leaders - CEBL
CODE: WGAC-AWS-CEBL
Data Warehousing on AWS - DWAWS
CODE: WGAC-AWS-DWAWS
Exam Readiness: AWS Certified Advanced Networking - Specialty - ACANS-EX
CODE: WGAC-AWS-ACANS-EX
Exam Readiness: AWS Certified Security - Specialty - ACSS-EX
CODE: WGAC-AWS-ACSS-EX
Architecting on AWS Accelerator - ARCH-AX
CODE: WGAC-AWS-ARCH-AX
Security Engineering on AWS - AWSSO
CODE: WGAC-AWS-AWSSO
Planning and Designing Databases on AWS - PD-DB
CODE: WGAC-AWS-PD-DB
Running Containers on Amazon Elastic Kubernetes Service - RCAEKS
CODE: WGAC-AWS-RCAEKS
Exam Readiness: AWS Certified DevOps Engineer – Professional - ACDOEP-EX
CODE: WGAC-AWS-ACDOEP-EX
AWS Cloud Practitioner Essentials - CP-ESS
CODE: WGAC-AWS-CP-ESS
Architecting on AWS - AWSA
CODE: WGAC-AWS-AWSA
Advanced Architecting on AWS - AWSAA
CODE: WGAC-AWS-AWSAA
AWS Discovery Day - AWSDD
CODE: WGAC-AWS-AWSDD
AWS Well-Architected Best Practices - WABP
CODE: WGAC-AWS-WABP
Systems Operations on AWS - AWSSYS
CODE: WGAC-AWS-AWSSYS
We use cookies to understand how you use our site and to improve your experience. To learn more, click here. Read our revised Privacy Policy and Terms and Conditions.