705.603.82 - Creating AI-Enabled Systems

Course Structure

Course Structure

This course is organized around five major Topics, each focused on the study of a particular machine learning (ML) system. Every Topic culminates in a case study submission, where you will demonstrate a deeper understanding of the system through either an Implementation Assignment or a Design/Analysis Assignment. This case study-driven approach allows you to engage with complex issues, experiment with systems, and draw meaningful insights about real-world ML applications.

• Lecture & Materials: Each Topic’s materials will be available at the start of its corresponding Module. You are encouraged to preview all sections early to guide your learning.
• Discussion Posts: Every week, you will complete a short discussion post to reflect on course material or applications. Posts are due at the end of each week (Sunday), and you must also submit a response to a peer’s post by the following Friday. These discussions are designed to foster engagement, critical thinking, and collaboration.

Case Study Submissions

Each Topic culminates in a case study assignment involving Implementation and Design/Analysis work:

• The Implementation Assignments (4 total) require you to build a prototype of the system introduced in lecture. These assignments will be completed through GitHub Classroom using an autograder system, where you will implement specific system components according to the provided architecture. The autograder will run unit tests against your code, offering immediate feedback on correctness and completeness to ensure your implementation meets the required specifications.
• In contrast, the Design/Analysis Assignments (4 total) focus on using Jupyter notebooks to experiment with the system, analyze results, and draw meaningful insights. These assignments emphasize interpreting data, exploring trade-offs, and connecting design decisions to observed system performance.Details: These assignments emphasize interpreting data, exploring system trade-offs, and connecting design decisions to observed performance.

Course Goals

This course prepares students to design and build AI-enabled systems by considering the full lifecycle of AI development—from requirements gathering to deployment. Students will gain experience examining the challenges and trade-offs involved in requirements definition, data collection and preparation, metrics design, model selection and training, system integration, and deployment in real-world environments.

A central goal of the course is to demonstrate how these individual components come together to form a deployable AI pipeline, moving beyond isolated models to complete, functioning systems. By working through case studies, students will learn to both implement system prototypes and analyze system designs, developing a practical understanding of how theoretical concepts translate into engineering practice.

In addition, students will strengthen their ability to:

• Critically evaluate system requirements and their impact on design decisions.
• Select and justify appropriate metrics for assessing performance.
• Understand how data quality, availability, and bias affect system outcomes.
• Connect modeling approaches to downstream deployment concerns.
• Communicate technical insights through both code and analysis.

By the end of the course, students will have developed the skills and mindset necessary to engineer robust, scalable AI solutions that are not only technically sound but also responsive to real-world constraints and goals.

Course Learning Outcomes (CLOs)

• Utilize the end-to-end lifecycle of creating, deploying, and operating AI-enabled systems
• Employ strategies to decompose problem sets for applying AI capabilities to improve operations
• Perform data engineering tasks and design a data system to support an AI-enabled system
• Select AI algorithms to address the requirements to design an AI-enabled system
• Apply evaluation metrics to include addressing bias to test AI-enabled systems
• Explain the trade-space of different deployment strategies to include continuous monitoring techniques
• Create a portfolio of machine learning solutions available in GitHub

Textbooks

No textbook is required. Each module will have a reading list that will include papers and textbooks chapters from O’Reilly online. Please use your JHED ID for logging into O’Reilly (https://www.oreilly.com). This is an excellent source for books and interactive tutorials. I encourage your exploration of such a valuable tool. If students are interested in learning more about the materials related to the basic foundations of this course, these books are recommended:

• Designing Machine Learning Systems by Chip Huyen
• Machine Learning Design Patterns by Valliappa Lakshmanan, Sara Robinson, Michael Munn

Required Software

Assignments require a python development environment to generate Jupyter notebooks and python scripts. A tutorial on Jupyter notebooks can be found here.

Docker (docker.com) is required to create the necessary development environment and more. The class 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. A video tutorial can be found here. You will also use docker to create deployable images to run your work. There is an interactive docker tutorial available here.

Docker containers avoid library and operating system conflicts to one to also run the containers regardless of their environment (as long as it supports docker). With git and docker you will build an accessible and managed machine learning portfolio of your classwork. Docker also allows straightforward and isolated installation of other software discussed in the course such as NoSQL databases (e.g. MongoDB) and messaging software (e.g. Kafka). Docker images/containers are easy to remove when no longer needed. Docker containers are far superior than installing and deinstalling software and all the various python libraries directly in your computer operating system.

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

It is expected that each case will take approximately 40–50 hours to complete. This course consists of case study submissions (i.e., implementation and analysis/design), and discussions designed to reinforce learning and build toward a final integrative project.

All submissions are due according to the dates in the Course Calendar. Unless otherwise specified, submissions should be uploaded to GitHub and/or Canvas. You will receive instructions for how repositories should be structured for assignments.

Implementation Assignments (25% of Final Grade)

Implementation assignments focus on building system prototypes aligned with the architectures presented in lecture. These assignments are distributed through GitHub Classroom and graded using an autograder system. The autograder will execute unit tests on your code, providing immediate feedback on correctness, completeness, and adherence to the required specifications. These assignments emphasize hands-on software delivery, giving students practical experience constructing core ML system components.

Late submissions will be reduced by 10 points (out of 100) for each week late. Deductions are NOT prorated, and exceptions will only be made with prior coordination with the instructors.

Design/Analysis Assignments (40% of Final Grade)

Design and analysis assignments emphasize system experimentation and critical thinking. Using Jupyter notebooks, students will analyze system behavior, evaluate trade-offs, and draw insights that connect theoretical concepts to observed performance. Submissions will include written explanations supported by figures, tables, and results from experiments. These assignments develop the ability to interpret data, assess design alternatives, and communicate technical findings effectively.

Late submissions will be reduced by 10 points (out of 100) for each week late. Deductions are not prorated, and exceptions will only be made with prior coordination with the instructors.

Final Case Study (25% of Final Grade)

The final case study is a culminating assignment that provides an in-depth exploration of a specific ML system. Beginning with an open-ended description of a hypothetical problem, students will propose and justify an ML-system solution. The process includes identifying requirements, gathering information, brainstorming alternatives, evaluating trade-offs, and developing a proposed design.

Final submissions will be delivered as a Markdown report (along with supporting Jupyter) that includes:

1. A clear description of the problem.
2. A detailed proposed system design.
3. An argument for how the design addresses requirements.
4. Hypotheses and evaluations of the proposed design.

The report should include diagrams, tables, charts, and other evidence to support your design decisions.

Discussion Participation (10% of Final Grade)

Weekly discussions supplement the course material and case studies by encouraging reflection and peer-to-peer engagement. Each week includes two requirements:

• Initial Post: A response to the discussion prompt, due by the end of the week (Sunday). Posts should be thoughtful and detailed, and may include supporting materials such as figures or graphs.
• Peer Responses: At least one response to a classmate’s post is due the following Friday after the initial post is due. Responses should provide constructive feedback, alternative perspectives, or additional insights while maintaining professionalism and respect.

Instructors will monitor discussions, occasionally providing feedback to guide conversation. Students are encouraged to refine their contributions based on feedback received.

Grading Policy

Final grades will be determined according to the following weighting:

Grades are typically posted one week after assignment due dates.

Grading Scale:

• 100–90 = A
• 89–80 = B
• 79–70 = C
• 69–63 = D
• <63 = F

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 is committed to providing welcoming, equitable, and accessible educational experiences for all students. If disability accommodations are needed for this course, students should request accommodations through Student Disability Services (SDS) as early as possible to provide time for effective communication and arrangements.  For further information about this process, please refer to the SDS Website.

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.