title
Please take a moment to fill out this form. We will get back to you as soon as possible.
All fields marked with an asterisk (*) are mandatory.
Home >
Course Catalog >
Amazon Web Services >
Machine Learning >
MLOps Engineering on AWS
MLOps Engineering on AWS
Duration
3 Days
Course
AWS-245
Available Formats
Classroom Training, Online Training
AWS Training Pass
Take advantage of flexible training options with the AWS Training Pass and get Authorized AWS Training for a full year.
Learn More
Course Description
Overview
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 organization through daily reflection of lesson and lab content, and through conversations with peers and instructors.
- Duration: 3 days, 8 hours/day
Objectives
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
Audience
This course is intended for any one of the following roles with responsibility for productionizing machine learning models in the AWS Cloud:
- DevOps engineers
- ML engineers
- Developers/operations with responsibility for operationalizing ML models
Prerequisites
-
What experience you'll need:
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
- The Elements of Data Science (digital course), or equivalent experience
- Machine Learning Terminology and Process (digital course)
Topics
Day 1
Module 0: Welcome
- Course introduction
- Machine learning operations
- Goals of MLOps
- Communication
- From DevOps to MLOps
- ML workflow
- Scope
- MLOps view of ML workflow
- MLOps cases
- 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
- 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
- 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
- Course review
- Activity: MLOps Action Plan Workbook
- Wrap-up
Related Courses
-
Deep Learning on AWS
AWS-170
- Duration: 1 Day
- Delivery Format: Classroom Training, Online Training
- Price: 675.00 USD
-
The Machine Learning Pipeline on AWS
AWS-185- Duration: 4 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,700.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
Course Added To Shopping Cart
bla
bla
bla
bla
bla
bla
Self-Paced Training Terms & Conditions
??spvc-wbt-warning??
??group-training-form-area??
??how-can-we-help-you-area??
??personalized-form-area??
??request-quote-area??
Sorry, there are no classes that meet your criteria.
Please contact us to schedule a class.
STOP! Before You Leave
Save 0% on this course!
Take advantage of our online-only offer & save 0% on any course !
Promo Code skip0 will be applied to your registration
Nothing yet
Purchase Information
??elearning-coursenumber?? ??coursename??
View Cart