Overview
Learn how to extend DevOps practices to build, train and deploy machine learning models.
- This course builds upon and extends the DevOps practice prevalent in software development to build, train, and deploy machine learning (ML) models.
- The course stresses the importance of data, model, and code to successful ML deployments.
- It will demonstrate the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations.
- The course will also discuss the use of tools and processes to monitor and take action when the model prediction in production starts to drift from agreed-upon key performance indicators.
Kornerstone is an AWS Authorized Training Partner to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings.
Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
- Describe machine learning operations
- Understand the key differences between DevOps and MLOps
- Describe the machine learning workflow
- Discuss the importance of communications in MLOps
- Explain end-to-end options for automation of ML workflows
- List key Amazon SageMaker features for MLOps automation
- Build an automated ML process that builds, trains, tests, and deploys models
- Build an automated ML process that retrains the model based on change(s) to the model code
- Identify elements and important steps in the deployment process
- Describe items that might be included in a model package, and their use in training or inference
- Recognize Amazon SageMaker options for selecting models for deployment, including support for ML frameworks and built-in algorithms or bring-your-own-models
- Differentiate scaling in machine learning from scaling in other applications
- Determine when to use different approaches to inference
- Discuss deployment strategies, benefits, challenges, and typical use cases
- Describe the challenges when deploying machine learning to edge devices
- Recognize important Amazon SageMaker features that are relevant to deployment and inference
- Describe why monitoring is important
- Detect data drifts in the underlying input data
- Demonstrate how to monitor ML models for bias
- Explain how to monitor model resource consumption and latency
- Discuss how to integrate human-in-the-loop reviews of model results in production
Prerequisites
Required:
- AWS-TE: AWS Technical Essentials
- AWS-DEVOPS: DevOps Engineering on AWS
- AWS-PDSASM: Practical Data Science with Amazon SageMaker
Target Audience
- ML data platform engineers
- DevOps engineers
- Developers/operations staff with responsibility for operationalizing ML models
Course Curriculum
Module 0: Welcome
- 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, 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
Dates & Locations
August 26, 2026 - August 28, 2026
Location:
Online
Format:
Live Virtual
Availability:
TBC
October 21, 2026 - October 23, 2026
Location:
Online
Format:
Live Virtual
Availability:
TBC
December 16, 2026 - December 18, 2026
Location:
Online
Format:
Live Virtual
Availability:
TBC
Exam & Certification
There is no exam directly associated with this course. However, AWS offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2026 and beyond. Achieving AWS credentials is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
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- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our top AWS Certifications for 2026 and start building the skills that matter today.
