Cloud Wizard - MLOps Engineering on AWS - 3 Days ILT

Cloud Wizard Consulting is an AWS Advanced Training Partner and Select Tier Consulting Partner with a track record of training more than 5,000 professionals. Our AWS Authorized Instructors hold all AWS certifications, bringing real-world production MLOps experience into the classroom. Participants benefit from small-group instruction, hands-on labs in sandboxed AWS environments, and post-course guidance to accelerate their ML production journey.

This 3-day instructor-led course is designed for:

• MLOps engineers who want to productionize and monitor ML models in the AWS cloud
• DevOps engineers responsible for deploying and maintaining ML models in production
• Teams seeking to implement repeatable, reliable ML workflows at enterprise scale

After completing this course, your team will be able to:

• Build and automate end-to-end ML pipelines using Amazon SageMaker Pipelines and AWS Step Functions
• Implement CI/CD workflows for machine learning that eliminate manual handoffs between data scientists and operations
• Monitor deployed models for data drift and performance degradation, then trigger automated retraining
• Apply multi-account strategies and traffic-shifting techniques for safe, scalable model deployments
• Enforce security and governance best practices across the ML lifecycle

The course follows the four-level MLOps maturity framework, focusing on the first three levels:

Module 1: Introduction to MLOps Understand the people, processes, and technology behind successful ML operations. Evaluate security and governance requirements for ML use cases.

Module 2: Initial MLOps - Experimentation Environments Set up ML experimentation environments in SageMaker Studio. Hands-on lab: Provision a SageMaker Studio environment with AWS Service Catalog.

Module 3: Repeatable MLOps - Repositories Manage data versioning, model version control, and code repositories to ensure reproducibility across your ML lifecycle.

Module 4: Repeatable MLOps - Orchestration Build ML pipelines with SageMaker Pipelines, orchestrate end-to-end workflows with Step Functions, and standardize with SageMaker Projects. Hands-on lab: Automate a workflow with Step Functions.

Module 5: Reliable MLOps - Scaling and Testing Implement multi-account strategies, test model variants, and shift traffic safely. Hands-on labs: Test model variants and shift production traffic.

Module 6: Reliable MLOps - Monitoring Monitor models for data drift, remediate performance issues, and build troubleshooting capabilities into your ML pipeline. Hands-on lab: Monitor a model for data drift.

• Provisioning a SageMaker Studio Environment with AWS Service Catalog
• Automating a Workflow with Step Functions
• Testing Model Variants
• Shifting Traffic
• Monitoring a Model for Data Drift
• Building and Troubleshooting an ML Pipeline

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

This course is delivered by AWS Authorized Instructors through Cloud Wizard Consulting. Contact us to discuss available dates, virtual or on-site delivery options, and group enrollment for your team.