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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.
The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors. -
Course Contents
-
- Module 0: Welcome
- Module 1: Introduction to MLOps
- Module 2: MLOps Development
- Module 3: MLOps Deployment
- Module 4: Model Monitoring and Operations
- Module 5: Wrap-up
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
You will receive the original course documentation by Amazon Web Services in English language as an e-book.
-
Target Group
-
This course is intended for any one of the following roles with responsibility for productionizing machine
learning models in the AWS Cloud:- DevOps Engineers
- ML Engineers
- Developers/operations with responsibility for operationalizing ML models
-
Knowledge Prerequisites
-
Required:
AWS Technical Essentials
DevOps Engineering on AWS
Practical Data Science with Amazon SageMaker
Recommended:
The Elements of Data Science
Machine Learning Terminology and Process -
Please note our overview AWS Trainings!
| 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 |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.-
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.
The instructor will encourage the participants in this course to build an MLOps action plan for their organization through daily reflection of lesson and lab content, and through conversations with peers and instructors. -
Course Contents
-
- Module 0: Welcome
- Module 1: Introduction to MLOps
- Module 2: MLOps Development
- Module 3: MLOps Deployment
- Module 4: Model Monitoring and Operations
- Module 5: Wrap-up
You have access to the labs for another 14 days after the course. This way you can repeat exercises or deepen them individually.
You will receive the original course documentation by Amazon Web Services in English language as an e-book.
-
Target Group
-
This course is intended for any one of the following roles with responsibility for productionizing machine
learning models in the AWS Cloud:- DevOps Engineers
- ML Engineers
- Developers/operations with responsibility for operationalizing ML models
-
Knowledge Prerequisites
-
Required:
AWS Technical Essentials
DevOps Engineering on AWS
Practical Data Science with Amazon SageMaker
Recommended:
The Elements of Data Science
Machine Learning Terminology and Process -
Please note our overview AWS Trainings!
| 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 |
-
Classroom training
- Do you prefer the classic training method? A course in one of our Training Centers, with a competent trainer and the direct exchange between all course participants? Then you should book one of our classroom training dates!
-
Online training
- You wish to attend a course in online mode? We offer you online course dates for this course topic. To attend these seminars, you need to have a PC with Internet access (minimum data rate 1Mbps), a headset when working via VoIP and optionally a camera. For further information and technical recommendations, please refer to.
-
Tailor-made courses
-
You need a special course for your team? In addition to our standard offer, we will also support you in creating your customized courses, which precisely meet your individual demands. We will be glad to consult you and create an individual offer for you.
You can find the complete description of this course with dates and prices ready for download at as PDF.
Online Training
Trainer language German