MLOps on AWS
With this course, you will discover what MLOps is and how you can apply it in AWS (Amazon Web Services). For example, you will learn more about AWS SageMaker, Elastic Container Service, CloudWatch, and more. This course is aimed at people with Python skills and general ML experience.
Looking to upskill your team(s) or organization?
Rozaliia will gladly help you further with custom training solutions.
Rozaliia Khafizova
Data and AI Training Advisor
What will you learn?
After the training, you will be able to:
Understand all the necessary components in an end-to-end ML system
Set up CloudWatch dashboards for your application
Create and trigger machine learning pipelines with SageMaker
Integrate and deploy all code through a CI/CD pipeline with Github Actions
Deploy your model as scalable API with FastAPI, Docker and ECS Fargate
Program
- Discover key MLOps principles
- Create a solution design
- Get started with the cloud tooling
- Experiment tracking
- Training jobs and pipelines
This training is for you if:
You already have a solid understanding of ML, and want to take your models outside of the development phase
You want to incorporate best practices from Software Engineering
You already have foundational software engineering skills (Python, Git, Docker)
You want to learn more about AWS and MLOps
This training is not for you if:
You want to learn how to develop ML models (check out the Certified Data Science with Python or the Advanced Data Science with Python trainings)
You do not have basic programming experience (check out our Python for Data Analyst course)
You want to learn general methods for developing production-ready applications without focusing on a specific public cloud (check out our Production-Ready Machine Learning course)
Your primary interest is in (exploratory) research; this course is geared towards ML engineering
You are interested in a different cloud service (check out our MLOps on Azure or our MLOps on GCP training)
Why should I do this training?
Learn best practices about deploying machine-learning applications on GCP
Hands-on training with real life examples – learn today apply tomorrow!
Get taught by machine-learning experts that love to teach in a very fun and interactive learning environment
What else should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, the trainer will get in touch to ask you about any requirements you may have and any pre-course tasks you will need to do.
See you soon!
Training information
All literature and course materials are included in the price
Information on the software and tooling will be shared before the start date
This course requires a laptop
Online courses are delivered via Zoom or Microsoft Teams
Meet the Trainers
Jetze Schuurmans
Jetze is a well-rounded Machine Learning Engineer who is as comfortable solving Data Science use cases as he is productionizing them in the cloud. His interests include MLOps, GenAI and Cloud Engineering.
Yke Rusticus
Yke is an experienced machine learning engineer who has helped various companies generate value from their machine learning solutions. As trainer, his goal is to make sure you learn how to get your solutions beyond the experimentation phase, and into the real world.
Also interesting for you
View all training coursesThis MLOps on Azure training is then a perfect next step if you’d like like to take your Machine Learning models further.
Discover what MLOps is and how you can apply it in GCP (Google Cloud Platform) with our MLOps on GCP training course.
