Course Information


This course is an advanced graduate course in cloud computing and machine learning. This course exposes students to various cloud computing models and introduces them to performing machine learning on the cloud. The course material introduces students to various cloud providers including Amazon AWS, Google GCE, and IBM Cloud and their machine learning service capabilities. Students will learn how to build cloud systems for machine learning, the application characteristics, and develop hands-on experience with programming machine learning applications on these cloud platforms.


The course does not follow a specific textbook.
Reference book: Cloud Computing for Machine Learning and Cognitive Applicaitons ISBN: 9780262036412.
Research papers and other important material on relevant topics will be made available during the course.

Topics (tentative)

    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

    Micro services, DevOps

    Performance characterization

    Distributed deep learning

    Disaggregrated systems and computing

    Invited talks


CSCI-GA.1170 Fundamental Algorithms

Understanding of important algorithms such as sorting, searching, graphs, etc.

CSCI-GA.2110 Programming Languages

Understanding of the design, use, and implementation of imperative, object-oriented, and functional programming languages.

CSCI-GA.2250 Operating Systems

Understanding of Computer Architecture, C/C++ programming, OS design, process, stack/heap, threads, file-system, IO, Networks.

Python programming

Intermediate programming skills.


There are no exams for this class.
Grades are based on the project work, homework assignments, class presentation, and class participation.

Two projects

Project 1: 20%
Using existing database of information, DNN topology and SaaS to train for a target.

Project 2: 30%
Identify a problem that DNN training can be applied. Implement the training in the cloud computing.

Class presentations


Two students per group

Class participation


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.



Six homework assignments are expected.