Level up your data, AI/ML, and infra skills!
Data Dan (Daniel Whitenack) is a PhD-trained data scientist with over ten years of experience developing ML/AI applications in industry. Through his courses and workshops, Dan has helped hundreds of students learn the theory and practice of machine learning, AI, data engineering, and analytics.
Developing AI with PyTorch
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.
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Coming soon
Docker-izing Data Science Applications
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
View course
Coming soon
Developing AI with PyTorch
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.
View course
Coming soon
Docker-izing Data Science Applications
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
View course
Coming soon