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MLOps Engineering on AWS

Build upon and extend the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models.

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/21 hours of instruction
Public Classroom Pricing

Starting at: $2025(USD)

Group Rate: $1925

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

Part 1: Introduction to MLOps

  1. Machine learning operations
  2. Goals of MLOps
  3. Communication
  4. From DevOps to MLOps
  5. ML workflow
  6. Scope
  7. MLOps view of ML workflow
  8. MLOps cases

Part 2: MLOps Development

  1. Intro to build, train, and evaluate machine learning models
  2. MLOps security
  3. Automating
  4. Apache Airflow
  5. Kubernetes integration for MLOps
  6. Amazon SageMaker for MLOps
  7. Lab: Bring your own algorithm to an MLOps pipeline
  8. Demonstration: Amazon SageMaker
  9. Intro to build, train, and evaluate machine learning models
  10. Lab: Code and serve your ML model with AWS CodeBuild
  11. Activity: MLOps Action Plan Workbook

Part 3: MLOps Deployment

  1. Introduction to deployment operations
  2. Model packaging
  3. Inference
  4. Lab: Deploy your model to production
  5. SageMaker production variants
  6. Deployment strategies
  7. Deploying to the edge
  8. Lab: Conduct A/B testing
  9. Activity: MLOps Action Plan Workbook

Part 4: Model Monitoring and Operations

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

Part 5: Wrap-up

  1. Course review
  2. Activity: MLOps Action Plan Workbook
  3. Wrap-up

  • 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
  • Recommended: Machine Learning Terminology and Process (digital course)

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

  • 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

A full refund will be issued for class cancellations made at least 10 business days before the course begins. Payment is nonrefundable for cancellations or reschedules made within 10 business days from the course start date and for NoShows (students who do not attend class).
For reschedules made within 10 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of six months from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.
 

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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Nov 1st - 3rd, 2022
9:00 AM - 5:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Nov 15th - 17th, 2022
12:00 PM - 8:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Dec 13th - 15th, 2022
9:00 AM - 5:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Jan 10th - 12th, 2023
9:00 AM - 5:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Mar 8th - 10th, 2023
12:00 PM - 8:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Apr 19th - 21st, 2023
9:00 AM - 5:00 PM ET
$2025(usd)
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Please call Cprime for location details
at 1-877-800-5221
, LOL', 'gtr' => '', ))

Live OnlineUSA Based

Jun 14th - 16th, 2023
9:00 AM - 5:00 PM ET
$2025(usd)

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