705.604.8VL - Production Artificial Intelligence (AI) Systems
Description
Build production-level AI systems through gaining four critical skills: (1) Automating model development via newly developed and existing data/model pipelines (2) Selecting, adapting, and optimizing newly developed and existing models for performance and computing resource use via feature selection, model architecture, hyperparameter tuning, metrics introspection, transfer learning, and ensemble creation. (3) Form a complete system through the integration of supporting technologies such as high speed messaging, real-time APIs, distributed/parallel processing, data storage (NoSQL), and system management. (4) Packaging into a deployed system via containerization and orchestration including post-deployment adaptation across heterogeneous computing fabrics (cloud to edge). The student acquires these skills through developing a realistic, hands-on, collaborative, and incremental project that produces optimized models integrated into an operational system deployed across hybrid computing environments.
Instructor
John Hebeler
Course Structure
Production AI Brief Outline Spring 2025
Date (mod) | Domain (%) | Topic/Requirement | I/T* | Wt % | Module Due** |
1/21 (1) | Foundation (5) | Project Statement | T | 5 | 2 |
Data (25) | 25 | ||||
1/28 (2) | Data Selection and Rationale | I | 5 | 3 | |
Data Ingest - both archival and real time | I | 5 | 3 | ||
2/4 (3) | Data Cleanup and Analytics Pipeline | I | 10 | 4 | |
2/11 (4) | Data Persistence | I | 5 | 4 | |
Model (35) | 35 | ||||
2/18 (5) | Model Candidates Selection (minimum 2) | I | 10 | 5 | |
2/25 (6) | Model Performance Determination | I | 5 | 6 | |
Model Resource Use Determination | I | 5 | 6 | ||
3/4 (7) | Model Selection, Inference Export, and Governance | I | 15 | 7 | |
System (15) | 15 | ||||
3/11 (8) | System Scalable Architecture and Jira setup | T | 5 | 8 | |
3/18 | SPRING BREAK | ||||
3/25 (9) | Integrated Distributed System with Inference analytic | T | 5 | 9 | |
4/1 (10) | System Monitoring | T | 5 | 10 | |
Deployment (15) | 15 | ||||
4/8 (11) | Containerization | T | 5 | 11 | |
4/15 (12) | Orchestration and Deployment | T | 10 | 12 | |
4/22 (13) | Model Drift and Update | T | 5 | 13 | |
4/29 (14) | Final (5) | Presentation | T | 5 | 14 |
* Individual (60%) or Team (40%)
** Due at end of the module - except for the project statement, all deliverables include a jupyter notebook overview and python code files preferable organized by class
Course Topics
By the end of the course, the student acquires the following skills and capabilities:
- Develop a data/machine learning pipeline to ensure consistent development/experimentation of machine learning models
- Leverage and provide machine learning governance to properly reuse/share/version models and associated data paths/analytics
- Optimize models for performance and computing resources via experimentation, incorporation of model explanations, and leveraging share/existing models
- Integrate supporting technologies including data persistence, data/messaging communication, distribution/parallel processing, and system management
- Package and orchestrate the deployment of a machine learning system.
- Deploy and manage the machine learning system across various computing fabrics.
- Measure post-deployment system performance including model performance
- Update the deployed system due to system/model adaptation
Course Goals
Complete an advanced, end-to-end machine learning and inference system via: optimization of machine learning models for both performance and computer resource usage, integration of the optimized model into an complete system that integrates supporting technologies, deployment into various computer environments via proper packaging and orchestration, and post-deployment adaptation
Textbooks
Various book chapters and on-line tutorials/documentation as listed. All books are available through the Orielly account using a student’s Hopkins login credentials following this link: https://www.oreilly.com/member/login/ and use your John Hopkins email address. You also have the WSJ available using this link - https://education.wsj.com/search-students/ and search for John Hopkins.
Required Software
Up-to-date Google Chrome Browser and high speed internet connection. All design, development, and deployment is done in the Microsoft Azure Cloud resources.
Student Coursework Requirements
Grading consists of three components:
- Cloud Foundation Setup and Use (15%)
- Discussions and Participation (10%). This includes either running one discussion or AI show/tell during the semester
- Project (75%) comprised of many incremental assignments
Grading Policy
The course does not offer pluses and minuses.
A: >= 90
B: >= 80
C: >= 70
F: <70
Course Evaluation
All assignments offer a detailed rubric that indicates the expectations. Please review the rubric for each assignment prior to starting the assignment. If a rebru
Course Policies
The course has a rapid flow in order to achieve all the increments of the project. It is key that you keep pace with the various assignments.
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.