Level up your data, AI/ML, and infra skills!

It's no secret that AI applications are taking the world by storm. If you're a software engineer wanting to level up your skills, don't wait any longer. This is a complete crash course in applied ML/AI that is that is perfect for anyone with some programming experience. No math PhD required! By the end of the course, you will understand the machine learning development workflow and the most common models being utilized in industry. But, more importantly, you will be able to apply that knowledge to real world data. In fact, the course will walk you through the development of state-of-the-art models with scalable frameworks like PyTorch and through the deployment of those models to AWS.

In this course, we will learn how to put your data science applications in Docker images and run those as containers on any infrastructure. These skills will help you maintain reproducibility and increase efficiency as you deploy your applications, and they will help you standardize your code to better fit into modern infrastructures, CI/CD tools, and DevOps practices. We will cover: Why Docker, gettings started with Docker Docker-izing model training Docker-izing inference, services Managing and scaling Docker-ized data science apps