Embark on an advanced journey into the dynamic realm of cloud computing and machine learning. This graduate-level course explores the powerful synergy between these domains, covering essential fundamentals such as cloud infrastructure, artificial neural networks, Kubernetes orchestration, and practical applications. Students will delve into various cloud computing models, gaining insights into their capabilities. Additionally, they'll discover the art of performing machine learning on the cloud, including hands-on experience with cloud platforms. With guidance, students will not only build cloud systems for machine learning but also learn to characterize performance, ensuring proficiency in this ever-evolving technological landscape. Join us to unlock the full potential of cloud-based machine learning and elevate your expertise in this advanced field.
Research papers and other important material on relevant topics will be made available during the course.
Introduction to cloud computing
Introduction to machine learning on the cloud: Domains, Frameworks, Use cases
Getting started with machine learning on the cloud
Compute, network, storage infrastructure organization in the clouds
Artificial Neural Network
Virtual Machine, Container
Kubernetes
Performance characterization and modeling
Invited talks
Understanding of important algorithms such as sorting, searching, graphs, etc.
Understanding of the design, use, and implementation of imperative, object-oriented, and functional programming languages.
Understanding of Computer Architecture, C/C++ programming, OS design, process, stack/heap, threads, file-system, IO, Networks.
Intermediate programming skills.
The students are expected to know basic Linux commands and configurations.
Software communication and development tools such as Slack and Github will be used.
There are no exams for this class.
Grades are based on the project work, homework assignments, class presentation, and class participation.
Project 1: 20%
Using the existing dataset, DNN topology, and SaaS to train for a target with performance profiling/analysis.
Project 2: 30%
Identify a problem that DNN training can be applied to. Implement the training in the cloud computing.
10%
Two or three students per group
10%
This is a graduate-level class, so we are looking for your active participation in the class.
Your attendance alone does not qualify as active participation.
30%
5-6 homework hands-on assignments are expected.