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Amazon Web Services / AWS Machine Learning & AI
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MLOps Engineering on AWS (english)

3 days
English
In Kooperation mit
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.
Content

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.

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
Benefits
  • Deploying your own models in the AWS Cloud
  • Automating workflows for building, training, testing, and deploying ML models
  • The different deployment strategies for implementing ML models in production
  • Monitoring for data drift and concept drift that could affect prediction and alignment with business expectations

This course can be used as preparation for the following official AWS Certification: 
AWS Certified Machine Learning – Specialty.

Instructor
Yuri Nikulin
Matthew Millward
Methods

This course includes instructor lecture, presentations, hands-on labs, demonstrations, and group exercises/discussions.

Who should attend

This course is intended for the following job roles:

  • DevOps
  • Machine Learning & AI

We recommend that attendees of this course have the following prerequisites:

  • The Elements of Data Science (free digital course)
  • Machine Learning Terminology and Process (free digital course)

and have attended the following course (or equivalent knowlege):
The Machine Learning Pipeline on AWS.

1960
zzgl. MwSt.
2332.4
inkl. MwSt.
Buchungsnummer
33846
Ort der Veranstaltung
Online
2 Events
Inhouse Training
Firmeninterne Weiterbildung nur für eure
Mitarbeiter:innen - exklusiv und wirkungsvoll.
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In Kooperation mit
Starttermine und Details
24.4.2024
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
19.6.2024
Plätze frei
Durchführung gesichert
Durchführung fast gesichert
Plätze frei
Durchführung gesichert
Durchführung fast gesichert

The training is carried out in cooperation with an authorized training partner.
For the purpose of implementation, participant data will be transferred to the training partner and the training partner assumes responsibility for the processing of these data.
Please take note of the corresponding privacy policy.

Du hast Fragen zum Training?
Ruf uns an unter +49 761 595 33900 oder schreib uns auf service@haufe-akademie.de oder nutze das Kontaktformular.
1960
zzgl. MwSt.
Buchungsnummer
33846
2
Termine