AI-337-paul.wilmoth-2026-08-20-01-15-05
Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.
Title of Course [Machine Learning Operations and AI Systems Engineering]
Prefix and Number [AI 337]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites [AI 142, AI 260, and CPT_S 322 each with a grade of C or better.]
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [click here for best practices] [tbd]
Course Description
[This course focuses on building, deploying, and maintaining software systems that use machine learning. Starting with a trained model, students examine what is required to turn it into a reliable product that can operate under real-world conditions. Topics include requirements for AI-enabled systems, model and system quality, planning for incorrect predictions, system architecture, data quality, scalable data processing, model deployment, and testing in production. Students learn to build reproducible ML pipelines, version code, data, models, and configurations, track experiments, package models in containers, serve predictions through APIs, and automate testing and deployment. The course covers MLOps infrastructure, continuous integration and delivery, cloud-based deployment, monitoring, model updates, and experimentation in production. Students also examine data and concept drift, feedback loops, technical debt, system failures, and differences between offline evaluation and model behavior after deployment. Because production quality depends on more than prediction accuracy, the course considers latency, cost, scalability, reliability, safety, security, privacy, fairness, explainability, and usability. It also addresses how software engineers, data scientists, domain experts, and other stakeholders work together when developing AI-enabled products. Programming assignments, labs, case studies, and project milestones lead to a final project in which students build, deploy, evaluate, monitor, and improve an end-to-end ML system.]
Course Materials
Books: [
Required Textbook: Vijay Janapa Reddi, Introduction to Machine Learning Systems, Volume I: Foundations. MIT Press, 2026. The book is available open access at https://mlsysbook.ai/vol1/. Selected chapters will be assigned throughout the course. Additional readings and technical documentation will be provided through Canvas.
Recommended Books:
- Chip Huyen, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, O’Reilly Media.
- Noah Gift and Alfredo Deza, Practical MLOps: Operationalizing Machine Learning Models, O’Reilly Media.
- Valliappa Lakshmanan, Sara Robinson, and Michael Munn, Machine Learning Design Patterns, O’Reilly Media.
]
Other Materials: [Course readings, software instructions, datasets, and access to required computing or cloud resources will be provided by the instructor. ]
Fees: [None]
Monitor deployed models and identify problems related to data drift, model degradation, performance, and system reliability.
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
| Explain the major components of an end-to-end machine learning system and how they interact in a production environment (6a). | Lectures, architecture examples, case studies, and system-mapping exercises | Quizzes, examinations, and architecture assignment |
| Design reproducible data preparation, model training, validation, and deployment pipelines, and manage versions of code, data, models, system configurations, and experiments (2a, 2c, 2g, 6a, 6b, 6f). | Programming exercises, pipeline demonstrations, tool demonstrations, guided exercises, and hands-on labs | Assignments and course project |
| Package and deploy machine learning models using containers, APIs, and cloud or server-based environments, and apply continuous integration, continuous delivery, and workflow automation practices (2c, 2e, 2g, 6f). |
Deployment demonstrations, workflow design exercises, programming exercises, and hands-on labs |
Assignments and course project |
| Monitor deployed models and identify problems related to data drift, model degradation, performance, and system reliability (1b, 1d, 1e, 6b, 6c, 6d, 6e). |
Monitoring exercises, case studies, and analysis of deployed systems |
Assignments, exams and course project |
| Evaluate the scalability, reliability, security, and maintainability of an AI system (2b, 2c, 2d, 2e, 6b, 6e, 6f). | Architecture reviews, case studies, and system design activities | Assignments, exams, and project report |
| Identify ethical, privacy, fairness, governance, and security concerns that can arise throughout the AI system lifecycle (4a, 4f, 6e). |
Case discussions, readings, and responsible AI exercises |
Written assignments, class activities, and course project |
| Design and implement an end-to-end AI system that integrates data processing, model development, deployment, monitoring, and maintenance (2a, 2c, 2e, 2g, 6a, 6b, 6c, 6d, 6f). |
Incremental project milestones, technical demonstrations, and project development |
Final project, technical report, and presentation |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
|
Week 1 |
Introduction to MLOps and AI systems engineering; from models to systems; machine learning lifecycle |
Review an existing ML workflow and identify the components needed for production | In-class activity |
| Week 2 [dates] |
Model and system quality; testing beyond accuracy; goals and measurement | Analyze an ML product using technical and operational quality measures | Assignment 01 |
| Week 3 [dates] |
Requirements for AI-enabled systems; planning for incorrect predictions; risk analysis | Define requirements, risks, and expected failure conditions for an AI system | Assignment 02 |
| Week 4 [dates] |
Architecture and design of AI-enabled systems; initial model deployment | Develop the architecture and deployment plan for the course project | Project Proposal |
| Week 5 [dates] |
Data quality; model evaluation; testing ML pipelines and inference services | Design tests for data, model behavior, pipeline components, and the serving interface | Lab Assessment |
| Week 6 [dates] |
Infrastructure quality and MLOps; reproducible environments; Git; unit testing and continuous integration | Build a reproducible ML pipeline with automated tests and continuous integration |
Assignment 03 |
| Week 7 [dates] |
Model packaging, containers, APIs, and model serving | Containerize a model and deploy it as an inference service | Assignment 04 |
| Week 8 [dates] |
Testing and experimentation in production; A/B testing, canary releases, shadow deployment, and rollback | Design a controlled strategy for comparing model versions in production | Mid-Term Assessment |
| Week 9 [dates] |
Experiment tracking, model registries, versioning, provenance, and reproducibility |
Deploy the initial system and document the lineage of its data, code, and model | Project Milestone 01 |
| Week 10 [dates] |
Scalable data storage and processing; batch and streaming systems; cloud infrastructure; technical debt | Evaluate scalability, resource use, cost, and technical debt in an ML architecture | Assignment 05 |
| Week 11 [dates] |
Monitoring, logging, telemetry, system observability, and debugging
|
Build a monitoring workflow for infrastructure health and model quality | Assignment 06 |
| Week 12 [dates] |
Data drift, concept drift, feedback loops, model degradation, retraining, and continuous deployment | Add monitoring, drift analysis, model updating, and recovery procedures to the project | Project Milestone 02 |
| Week 13 [dates] |
Data drift, concept drift, feedback loops, model degradation, retraining, and continuous deployment | Evaluate a deployed AI system for fairness and explainability | Case-study assessment |
| Week 14 [dates] |
Transparency and accountability; security, privacy, safety, governance, and threat modeling | Conduct a security, safety, privacy, and governance review of the project system | Project Milestone 02 |
| Week 15 [dates] |
Interdisciplinary collaboration; end-to-end system integration; course review and reflection | Complete, document, and present the production ML system | Final Project, Technical Report, and Presentation |
Expectations for Student Effort
[Describe how much time students should expect to invest in the course each week. Graduate courses should state: "For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class." Note that Global campus courses will automatically include credit hour equivalents in the syllabus.] [Students should expect to spend approximately three hours per week attending class and a minimum of six additional hours outside of class. Outside work may include assigned readings, programming exercises, assignments, project development, and preparation for assessments. The time required may vary depending on students’ prior programming and machine learning experience.]
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
| [Programming Assignments] | [300] | [30%] |
| [Labs and In-Class Activities] | [100] | [10%] |
| [Midterm Assessment] | [250] | [25%] |
| Final Project and Presentation | [350] | [35%] |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
[94–100] |
C | [73-76.9] |
| A- | [90–93.9] | C- | [70–72.9] |
| B+ | [87–89.9] | D+ | [65–69.9] |
| B | [83–86.9] | D | [60–64.9] |
| B- |
[80–82.9] |
F | [0– 59.9] |
| C+ | [77–79.9] |
[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
Attendance and Make-Up Policy
[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ] [Students are expected to attend all class meetings and participate in scheduled labs and in-class activities. Attendance itself is not graded; however, some activities completed during class may contribute to the final grade. Students who must miss class should notify the instructor as soon as possible and are responsible for reviewing missed material and completing any permitted make-up work. Make-up exams, labs, or in-class assessments will be allowed only for documented emergencies, university-approved absences, or other circumstances approved by the instructor. Arrangements should be made within a reasonable period after the absence.]
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-[Receive a grade of zero on the affected assignment or assessment. Serious or repeated violations may result in failure of the course.]
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.