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?
Brijesh will gladly help you further with custom training solutions.
Brijesh Kohli
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)
