705.603.82 - Creating AI-Enabled Systems

Artificial Intelligence
Spring 2024


Achieving the full capability of AI requires a system perspective, extending beyond the models, to effectively leverage algorithms, data, and computing power. Creating AI-enabled systems includes thoughtful consideration of an operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. The objective of this course is to bring a system perspective to creating AI-enabled systems. The course will explore the full-lifecycle of creating AI-enabled systems starting with problem decomposition and addressing data, development, design, diagnostic, and deployment phases. Each module will either introduce a domain in Machine Learning (Tabular, Computer Vision, Natural Language Processing, and Physical Systems) or delve into the end-to-end development of a specific AI system. Students will be exposed to the common technologies and resources practitioners use to develop these systems.


Profile photo of John Hebeler.

John Hebeler

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.  The course is undergoing a major update so please do not go more than 1 module beyond the current module for things are likely to change.

Course Topics

Overview and Foundation Setup

ML System Overview and Generative AI assistance

Data Libraries Review

Data - Computer Vision and Time Series

Data - Unstructured Data

Data - Categorical Data

Machine Learning Model Libraries Review

Design Supervised Algorithms Part I

Design Supervised Algorithms Part II

Design Unsupervised Algorithms

Design Reinforcement Algorithms

Testing Metrics and Bias

 AI Ethics

AI System Deployment

Course Goals

This course prepares students for creating AI-enabled systems by considering the full-lifecycle for building practical solutions.

Course Learning Outcomes (CLOs)


No textbook is required. Each module will have a reading list that will include papers and textbooks from O’Reilly online. Please use your JHED ID for logging into O’Reilly (https://www.oreilly.com)

Required Software

Establishing your portfolio establishes a populated Jupyter lab to develop Jupyter notebooks and python scripts. A tutorial on Jupyter notebooks can be found here: https://www.dataquest.io/blog/jupyter-notebook-tutorial/.

Docker (docker.com) is required to create the necessary development environment and more.  The course provides the necessary instructions in module 1.  This allows the creation of a docker container that contains jupyter lab, an environment to create and run jupyter notebooks and python scripts.  This container also includes most of the python libraries required for the assignments and git, a version control for your created software.  There is a git video in safari at https://learning.oreilly.com/videos/complete-git-guide/9781800209855/ .  You also use docker to create deployable images to run your work.

You are required to have access to an operating system that supports Docker. Current Windows, Mac, and Linux all support Docker.  If this is not available, you can access cloud virtual machines and install docker there.

Student Coursework Requirements

Assignments are due according to the dates posted in your Canvas course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. Grades typically post within 2 weeks after submission.

Grading Policy

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.  Most assignments contain a detailed rubric that outlines the specific requirements and their associated weights.  It is highly recommend that you reference the rubric while addressing the assignment.  It is no excuse, if you didn't examine the rubric and lost points due to a missing requirement.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

The course will use the following grading system:

100-90 = A 89-80 = B 79-70 = C 69-63 = D

Final grades will be determined by the following weighting:


% of Grade





AI Enabling Technology


System Project


Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.