Systems & Toolchains for AI Engineers

Catalog description

Adopting Artificial Intelligence in modern era has much more to it than learning the theoretical foundation of AI algorithms. The implementation of Machine Learning and Artificial Intelligence at large scale requires solid technical infrastructure to support its complex, heavy processes. In this course, students will learn to be effective users of AI systems. Students will gain hands-on experience with modern ML frameworks and infrastructure tools, in the context of large real-world datasets and under conditions requiring engineering design choices. This experience will be gained in conjunction with practical application of class topics in a real cloud environment. The premise of this course is to build a broad and solid foundation in Artificial Intelligence Infrastructure that will pay significant dividends throughout a student's research and work career across data science and Artificial Intelligence related fields. In this class, we will focus on the following topics: Data Collection and Storage; Data Streaming; Data Engineering; Modern ML Frameworks; Model Validation and Monitoring; Neural Network Design and Implementation; Embedded Machine Learning; Deployment of ML Models to the Cloud. The course material will focus on recent and landmark research papers and existing tools and software systems. Students will have substantial programming project work in which they design, implement, and analyze aspects of AI-model infrastructure. This course will use an IoT-relevant dataset throughout the semester. The format of this course will be a mix of lectures and hands-on labs. Students will be responsible for readings, and completing a hands-on project focused on developing applications on Apache Spark, TensorFlow, Apache Kafka, and PostgreSQL. Readings will be selected from recent conference proceedings and journals.