635.603.82 - AI/ML Ops

Course Structure

The course content is divided into modules. A module will have three sections: Overview, Content, and Assignments. Each week a new module will be covered. Students should regularly check the Course Canvas for assignment due dates. Students are encouraged to preview all sections of a module on Canvas before each module.

Course Topics

• Explain the fundamentals and significance of AI, MLOps, and Responsible AI in the modern business landscape, including ethical considerations, risk management, and workforce impact.
• Analyze AI/MLOps concepts such as data strategy, DataOps, data preparation, privacy, and governance to support ethical and secure AI implementation.
• Develop strategies for operationalizing AI through AIOps frameworks, addressing integration and deployment challenges for reliable and scalable solutions.
• Design an AI adoption strategy that aligns business objectives, architecture, and infrastructure to support scalable, ethical, and integrated AI solutions.
• Construct data pipelines, feature stores, and production-level ML workflows for efficient model deployment and continuous monitoring.
• Evaluate model degradation using drift detection, ground truth evaluation, logging, and performance assessment to maintain adaptable and high-quality AI solutions.

Course Goals

This course is designed to provide an in-depth understanding of AI/ML Ops, a discipline that combines artificial intelligence (AI) and machine learning (ML) with operations and IT practices. The course covers the introduction to AI and ML Ops, including the framework for building and deploying AI/ML models, the infrastructure for ML, data management, and the road to AI adoption. The course also provides insights into model training and deployment, AI/ML Ops features, stages of AI/ML Ops, preparing for production, deploying to production, AI/ML security, governance, and future trends in AI/ML Ops. The course will conclude with practical applications of AI/ML Ops. By the end of the course, students will have the skills to design and implement an MLOps strategy for an organization.

Course Learning Outcomes (CLOs)

• Comprehension of AI and ML Ops Fundamentals
• Proficiency in AI/ML Ops Stages and Responsibilities
• Competence in MLOps Strategy Development
• Proficiency in AI/ML Ops Features Implementation
• Competence in MLOps Strategy Development

Textbooks

No purchased text is required, all content is available via this course site and arranged weekly.  All of the required readings have been provided in the course shell.

Student Coursework Requirements

• Weekly Individual Assignments: Each module will contain a graded assignment that students will complete individually. Weekly assignments will vary depending on module learning objectives and will include reviewing case studies, videos and articles and answering questions (30% of total grade)
• Team Project: Students will be assigned to a project team of 3 or 4 members. Each team will choose a topic to develop an MLOps project centered around addressing a specific problem statement. The project consists of three main stages
  • o Project Proposal – Due #Module 5
  • o Algorithm Selection – Due #Module 8
  • o Final Demo – Final Module

You can find comprehensive instructions for each component of the project in the Assignments tool. (30% of total grade)

• Discussion Activities: Weekly discussion activities where students will be asked to participate in asynchronous discussions on topics related to module learning objectives. (20% of total grade)
• Team Presentation: Four modules (#3, #6, #9, #12) will feature a graded team presentation. In these presentations, teams will be given a case study to review, summarize, and respond to any questions. (20% of total grade)
  

Discussion Requirements

The discussions will be graded for:
1. Frequency — Number and regularity of your discussion comments, and
2. Quality — Content of your contributions

Frequency - Number and regularity of your contributions. Students are expected to log into the course and post (respond) in each graded discussion topics on a minimum of three separate days per week, beginning no later than Thursday (week opens Wednesday).

Quality - Content of your contributions. Examples of quality posts include:
-  providing additional information to the discussion 
-  elaborating on previous comments from others
-  presenting explanations of concepts or methods to help fellow students
-  presenting reasons for or against a topic in a persuasive fashion 
-  sharing your own personal experiences that relate to the topic
-  providing a URL and explanation for an area you researched on the Internet.

Grading Policy

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range	Letter Grade
100-97	= A+
96-93	= A
92-90	= A−
89-87	= B+
86-83	= B
82-80	= B−
79-77	= C+
76-73	= C
72-70	= C−
69-67	= D+
66-63	= 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. 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. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. 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 EP Student Disability Services at 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 Student Conduct Code website.

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.