MLOps Engineering on AWS
Course Overview
This course is designed to provide you with the knowledge and skills you need to effectively build, train, and deploy machine learning (ML) models using AWS. You will learn how to use DevOps practices to automate the ML lifecycle, from data preparation to model deployment and monitoring.
The course covers the following topics:
Data preparation: You will learn how to prepare data for ML, including data cleaning, feature engineering, and data validation.
Model training: You will learn how to train ML models using various algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
Model deployment: You will learn how to deploy ML models to production using AWS services, such as Amazon SageMaker and Amazon Elastic Container Service (ECS).
Model monitoring: You will learn how to monitor ML models in production to ensure that they are performing as expected.
The course also includes hands-on labs that will give you the opportunity to apply what you have learned. Each lab is designed to help you build your MLOps skills and knowledge.
By the end of this course, you will be able to:
Understand the principles of MLOps.
Use DevOps practices to automate the ML lifecycle.
Train and deploy ML models using AWS services.
Monitor ML models in production.
This course is ideal for data scientists, software engineers, and DevOps engineers who want to learn how to build, train, and deploy ML models using AWS. It is also a valuable resource for anyone who wants to learn more about MLOps.
