MLOps Engineering on AWS
The MLOps Engineering on AWS (MLPOPS) course is designed for participants who want to learn how to automate the machine learning lifecycle. Participants will learn how to create, deploy, and monitor machine learning pipelines using fully managed services such as Amazon SageMaker, AWS Step Functions, and AWS Glue. Additionally, the course covers MLOps best practices and the role of automation tools in building scalable and repeatable machine learning pipelines. The course helps prepare for the AWS Certified Machine Learning – Specialty Certification .
Course Objectives
elow is a summary of the main objectives of the MLOps Engineering on AWS (MLPOPS) course :
- Learn to automate the machine learning lifecycle.
- Create, deploy, and monitor machine learning pipelines using AWS services such as Amazon SageMaker, AWS Step Functions, and AWS Glue.
- Cover MLOps best practices.
- Understand the role of automation tools in building machine learning pipelines.
- Build scalable and repeatable machine learning pipelines using automation tools and practices.
- Implement monitoring and logging for machine learning models to ensure performance and reliability.
- Manage model versioning and rollback strategies to handle model updates and changes.
- Integrate security practices and compliance requirements into MLOps workflows.
Course Certification
This course helps you prepare to take the:
AWS Certified Machine Learning – Specialty Exam ;
Course Outline
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
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, and Feature
- Store
- Solving the Problem(s)
- Activity: MLOps Action Plan Workbook
Module 5: Wrap-up
- Course review
- Activity: MLOps Action Plan Workbook
- Wrap-up
Course Mode
Instructor-Led Remote Live Classroom Training;
Trainers
Trainers are Amazon AWS accredited instructors and certified in other IT technologies, with years of practical experience in the sector and in training.
Lab Topology
For all types of delivery, the participant can access the equipment and actual systems in our laboratories or directly in international data centers remotely, 24/7. Each participant has access to implement various configurations, Thus immediately applying the theory learned. Below are some scenarios drawn from laboratory activities.
Course Details
Course Prerequisites
Participation in the following courses is recommended:
- AWS Technical Essentials
- DevOps Engineering on AWS
- Machine Learning Terminology and Process
- The Elements of Data Science
Course Duration
Intensive duration 3 days;
Course Frequency
Course Duration: 3 days (9.00 to 17.00) - Ask for other types of attendance.
Course Date
- MLOps Engineering on AWS (Intensive Formula) – On Request – 9:00 – 17:00
Steps to Enroll
Registration takes place by asking to be contacted from the following link, or by contacting the office at the international number +355 45 301 313 or by sending a request to the email info@hadartraining.com
