MLOps Engineering On AWS

at NetCom Learning

(19)
Course Details
Price:
$2,095 6 seats left
Start Date:

Mon, Nov 21, 10:00am - Nov 23, 6:00pm Eastern Time (3 sessions)

Location:
Live Online Training
Description
Class Level: Intermediate
Age Requirements: 16 and older
Average Class Size: 6
System Requirements: Your Computer
Use a recent PC or Mac computer that is less than 4 years old.Two display monitors are highly recommendedone for joining the virtual classroom and, if applicable, viewing digital booksthe other for doing your labs

Your internet connection
Please use a wired, not wireless or wifi connectionPlease use a broadband internet connection and not dial-up (modem)Computer USB headsets (with headphone & microphone in one unit) that we recommend: 
Microsoft LifeChat LX-3000 or Logitech ClearChat H390.
Class Delivery: Our instructors are passionate at teaching and are experts in their respective fields. Our average NetCom instructor has many, many years of real-world experience and impart their priceless, valuable knowledge to our students every single day. See our world-class instructors.

What you'll learn in this it class:

This MLOps Engineering on AWS course builds upon, and extends the DevOps practice prevalent in software development to building, training, and deploying 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 hand-offs between data engineers, data scientists, software developers, and operations.

In this MLOps Engineering on AWS training you 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.

Course Objectives

  • 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
  • Maximize confidence in the ML release process
  • Deployment operations
  • Identify potential security threats in ML and explain basic mitigation approaches
  • 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 Outline

  1. Course Overview
  2. Introduction to DevOps
  3. Infrastructure Automation
  4. AWS Toolsets
  5. Continuous Integration/Continuous Delivery (CI/CD) with Development Tools
  6. Continuous Integration/Continuous Delivery (CI/CD) with Development Tools
  7. Introduction to Microservices
  8. DevOps and Containers
  9. DevOps and Serverless Computing
  10. Deployment Strategies
  11. Automated Testing
  12. Security Automation
  13. Configuration Management
  14. Observability
  15. Reference architectures

Who Should Attend

  • DevOps engineers
  • ML engineers
  • Developers/Operations with responsibility for operationalizing ML models.

Prerequisites

Required

  • AWS Technical Essentials
  • Practical Data Science with Amazon SageMaker
  • DevOps Engineering on AWS

Recommended

  • The Elements of Data Science (digital course), or equivalent experience
  • Machine Learning Terminology and Process (digital course)

Course Outline

  1. Introduction
    1. Course Introduction
  2. Introduction to MLOps
    1. Machine learning operations
    2. The goals of machine learning operations (MLOps)
    3. The path from DevOps to MLOps
    4. Machine learning
    5. Scope
    6. An MLOps view of the Machine learning workflow
    7. Communication
    8. The value of MLOps: MLOps cases
  3. MLOps Development
    1. Intro to build, train, and evaluate machine learning models
    2. Automating
    3. Apache Airflow
    4. Kubernetes integration for MLOps
    5. Amazon SageMaker for MLOps
    6. Demonstration: Amazon SageMaker
    7. Intro to build, train, and evaluate machine learning models
    8. Demonstration: Lab overview
    9. Lab: Bring your own algorithm to an MLOps pipeline
    10. Group Activity: MLOps Action Plan Workbook
    11. Lab: Code and serve your ML model with AWS CodeBuild
  4. 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. Deployment security
    9. Lab: Conduct A/B testing
    10. Group Activity: MLOps Action Plan Workbook
  5. Model Monitoring and Operations
    1. The importance of monitoring
    2. Monitoring by design
    3. Lab: Monitor your ML model
    4. Human-in-the-loop
    5. Amazon SageMaker Model Monitor
    6. Demo: Amazon SageMaker Model Monitor
    7. Solving the Problem(s)
    8. Group Activity: MLOps Action Plan Workbook
  6. Wrap-up
    1. Course review
    2. Group Activity: MLOps Action Plan Workbook
    3. Wrap-up

Remote Learning

This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.

Upon registration, the instructor will send along additional information about how to log-on and participate in the class.

Still have questions? Ask the community.

Start Dates (1)
Start Date Time Teacher # Sessions Price
10:00am - 6:00pm Eastern Time TBD 3 $2,095
This course consists of multiple sessions, view schedule for sessions.
Tue, Nov 22 10:00am - 6:00pm Eastern Time TBD
Wed, Nov 23 10:00am - 6:00pm Eastern Time TBD

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