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
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 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.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Course Enquiry
