MLOps Engineering on AWS

This course builds on and extends the DevOps methodology used in software development to build, train and deploy machine learning (ML) models. In this three days course you will learn about the four-level MLOps maturity framework. It outlines the importance of data, model and code to successful ML deployments. The course also discusses the use of tools and processes to monitor and take action when the model prediction shifts from the key performance indicators. The course is intended for MLOps engineers who want to produce and monitor ML models in the AWS Cloud. It is also for DevOps engineers who are responsible for deploying and maintaining ML models. We recommend you to have completed AWS Technical Essentials, DevOps Engineering on AWS and Practical Data Science with Amazon SageMaker The program is taught with the help of presentations, hands-on labs, demonstrations and group activities. You will be able to prepare for the AWS Certified Machine Learning certification

45,000

In this course, you will learn to:

  • Explain the benefits of MLOps
  • Compare and contrast DevOps and MLOps
  • Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies
  • Set up experimentation environments for MLOps with Amazon SageMaker
  • Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code)
  • Describe three options for creating a full CI/CD pipeline in an ML context
  • Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code)
  • Demonstrate how to monitor ML based solutions
  • Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion; detects performance degradation; and re-trains the model on top of
    newly acquired data

This course is intended for:

  • MLOps engineers who want to productionize and monitor ML models in the AWS cloud
  • DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production

We recommend that attendees of this course have:

  • AWS Technical Essentials (classroom or digital)
  • DevOps Engineering on AWS, or equivalent experience
  • Practical Data Science with Amazon SageMaker, or equivalent experience

Module 0: Introduction

  • 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

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

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 & Feature Store
  • Solving the Problem(s)
  • Activity: MLOps Action Plan Workbook

Module 5: Wrap-up

  • Course review
  • Activity: MLOps Action Plan Workbook
  • Wrap-up
Why choose Cloud Wizard
  • Advanced Tier Training Partner
  • Amazon Authorised Instructors
  • Official AWS Content
  • Hands-on Labs
Class Deliverables
  • E-Content kit by AWS
  • Hands-on labs
  • Class completion certificates
  • Exam Prep sessions

Dates Available - Click on Book Now to proceed

Classroom3 daysAll DayMay 8, 202445,000
Classroom3 daysAll DayMay 22, 202445,000
Classroom3 daysAll DayJune 12, 202445,000
Classroom3 daysAll DayJune 26, 202445,000
Classroom3 daysAll DayJuly 10, 202445,000
Classroom3 daysAll DayJuly 24, 202445,000

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FAQs

To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order.

There is no minimum number of candidates required, we are happy to train 1 to 1 . With regards to the maximum number, we can accomodate 30 learners in one batch.

  1. Training Delivered by an Amazon Authorized Instructor.
  2. AWS Content E-Kit
  3. Hands-on-labs for 30 days
  4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course Completion Certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day)

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