MLOps Engineering on AWS
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
Class Deliverables
- Amazon Authorised Instructors
- Official AWS Content
- Hands-on labs *(if available)
- Class completion certificates
- Exam Prep sessions
Class Schedule
Stay ahead with our comprehensive course schedule. Browse upcoming training sessions & reserve your spot to gain hands-on expertise & certification-ready skills.
Virtual | 3 days | All Day | January 8, 2025 | ₹45,000 | |
Virtual | 3 days | All Day | January 22, 2025 | ₹45,000 | |
Virtual | 3 days | All Day | February 12, 2025 | ₹45,000 | |
Virtual | 3 days | All Day | February 26, 2025 | ₹45,000 | |
Virtual | 3 days | All Day | March 12, 2025 | ₹45,000 | |
Virtual | 3 days | All Day | March 26, 2025 | ₹45,000 |
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FAQs
Find answers to commonly asked questions about our courses, certifications, schedules, and more.
Your cloud learning journey made simple and transparent.
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.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- 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)
FAQs
Find answers to commonly asked questions about our courses, certifications, schedules, and more.
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.
- Training Delivered by an Amazon Authorized Instructor.
- AWS Content E-Kit
- Hands-on-labs for 30 days
- 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)