705.604.8VL - Production AI – Engineered AI Solutions

Artificial Intelligence
Fall 2025

Description

This course goes beyond theory, offering hands-on experience in building AI systems with the mindset and pace of a modern AI startup. Using a project-driven approach, students learn to architect, develop, and deploy real-world AI solutions entirely in the cloud, leveraging tools like Microsoft Azure, Terraform, and other cutting-edge technologies central to today’s AI ecosystems. Students will incrementally build a production-grade, cloud-deployed AI system—individually and in teams—mirroring the end-to-end process of launching an AI startup. The course emphasizes not just tools, but the engineering mindset needed for building scalable, adaptable, and reliable AI systems. Key focus areas include: 1.) Data and Model Optimization – Streamlining data pipelines, adapting existing models, and using ensembles for efficiency and performance. 2.) System Integration – Developing distributed systems with messaging, NoSQL persistence, and robust monitoring. 3.) Cloud Deployment – Live updating through containerization and orchestration in a 100% cloud-based environment. By the end of the course, students will have built a portfolio-ready AI product and gained a deep, practical foundation in modern AI engineering for production.

Instructor

Profile photo of John Hebeler.

John Hebeler

Course Structure

Production AI Brief Outline Spring 2025

DateModCapabilityTools ReviewedAzure Component Due*System Component Due*
8/261Team Software Development. G/AI RoleVM, Terraform, Local Visual Studio Code and extensions, Cost Reporting, azure naming conventions, Azure Git Repository, Generative AI canvas - recordingsVM Deployment, Repository 
9/22End to End ExampleAzure DevOps, System Project Overview, Deploy the GPU container app to AKS as an end-to-endConfigured Boards with initial tasking, Cost Report 
9/93Designing and Communicating with ComponentsAzure Service Bus, Azure IotHub2 IOT device to combining host to reporting hostSystem Description
9/164ComponentizationDocker, DockerHub, Azure Container RegistryDocker component in ACRSystem Requirements
9/235OrchestrationAzure Kubernetes Service (AKS) and HELMDocker Component in AKSComponent Design with APIs
9/306CI/CD DeploymentCI/CD Azure DevopsCI/CD Pipeline 
10/77Data PipelineBulk Storage and real time ingest  
10/148Data PersistenceNoSQL and Azure Data Storage Check point for cloud and project
10/219Model Selection and TrainingGPU Access, mlflow Data Component
10/2810Model OptimizationMachine Configurations with mlflow  
11/411Model Governance & ExplainabilityAzure model registry, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) Model Component
11/1112System Distribution and IntegrationIntegrate using CI/CD Azure Devops  
11/1813System InterfaceSystem finalizations/tweaking, documentation publishing, including Website/API http call functionality System
11/25 THANKSGIVINGNo Class - No Office Hour  
12/214Demonstrations  Final System Presentation
     * Due at the end of the Mod.

Course Topics

By the end of the course, the student acquires the following skills and capabilities:




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:

  1. Discussions and Participation (10%)
  2. Module Assignments (30%)
  3. Team System Components (45%)
  4. System Presentation(15%)
Late Policy: Every Week or portion of a week results in one grade loss.

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 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.