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 (*where available)
- Class completion certificates
- Exam Prep sessions
Dates Available - Click on Book Now to proceed
Virtual | 3 days | All Day | October 9, 2024 | ₹45,000 | |
Virtual | 3 days | All Day | October 28, 2024 | ₹45,000 | |
Virtual | 3 days | All Day | November 6, 2024 | ₹45,000 | |
Virtual | 3 days | All Day | November 20, 2024 | ₹45,000 | |
Virtual | 3 days | All Day | December 4, 2024 | ₹45,000 | |
Virtual | 3 days | All Day | December 19, 2024 | ₹45,000 |
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Popular Courses
This course is designed for individuals with little to no experience on the AWS Cloud. The learners will learn about AWS Cloud concepts, AWS services such as Security, AWS Architecture, Pricing and Support to develop their knowledge on the AWS Cloud.
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Learn to identify services and features required to build secure and highly available IT solutions in the AWS Cloud. Identify basic practices of AWS Architecture and process of designing optimal IT solutions using the AWS Well-Architected framework.
You will learn how to use a combination of DevOps best practices and tools to support your organization’s capability to develop, deliver and maintain applications and services at a high velocity on the AWS cloud.
The course explores the usage of the iterative Machine Learning (ML) pipeline to solve real-world business problems in a project-based environment. You will learn about each phase of the pipeline from an experienced AWS instructor.
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.
- 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)