RFP: Course Development, Online Course in Machine Learning Systems Engineering
The Master of Information and Data Science program at the School of Information at UC Berkeley seeks proposals for an online graduate course in Machine Learning Systems Engineering.
About the Course
This 14-week master's-level online course explores the design, deployment, and operation of production machine learning systems using modern infrastructure and orchestration platforms. Emphasizing Kubernetes, cloud-native architectures, and MLOps practices, the course should provide practical experience building and deploying scalable ML inference services, managing containerized workloads, and implementing observability for production systems. The course should aim to bridge academic understanding of distributed systems with applied industrial practice in ML engineering.
The instructor should assume that students are early in their data science or related careers, with prior coursework in statistics or machine learning, proficient in Python, and familiar with ML libraries (e.g., scikit-learn, PyTorch). Students are expected to be self-motivated and capable of hands-on experimentation in containerized and cloud environments. Students should not be assumed to have strong fundamentals in Kubernetes or cloud infrastructure.
The core topics should cover foundations of containerization and API development, including multi-stage builds, dependency management, and building inference services. Storage patterns for ML models should address the progression from baked-in models to volume mounts, with discussion of cloud-backed persistent storage in production environments.
Other topics should include:
- Distributed systems fundamentals including stateful versus stateless architectures, failure modes, and scaling implications
- Caching strategies incorporating model versioning, TTL-based expiration, and cache invalidation patterns
- Kubernetes orchestration from fundamental concepts through services, DNS resolution, and namespace isolation
- Production hardening including init containers and health probes tuned for ML workloads
- Cloud infrastructure migration covering managed Kubernetes, container registries, and separation of code and model versioning
- Deployment strategies and rollback procedures
- Performance engineering including load testing, metrics instrumentation, autoscaling, and bottleneck identification
Other topics that could potentially be considered include:
- Secrets management
- Infrastructure templating and configuration management
- Structured logging and distributed tracing
- Policy enforcement
The successful proposal will be accepted for development and offered in the MIDS online degree program. Typical MIDS courses have 1.5 hours per week of pre-recorded asynchronous content.
About the MIDS Program
The Master of Information and Data Science (MIDS) online program prepares students with the data science skills to assume leadership positions and drive innovation in the field.
Deliverables for Accepted Proposal
Instructors of accepted course proposals will be expected to produce a well-designed, reusable Canvas course. Instructors will collaborate closely with an instructional designer and video producer to ensure the course meets established quality standards and fully aligns with defined learning objectives and outcomes. This partnership is integral to creating a high-impact, student-centered online learning experience.
Submission Requirements
Respondents to this RFP must submit a cover letter and course proposal using the form below. The course proposal should contain at minimum a course description, weekly topic breakdown for a 14-week course, brief descriptions of assignments, grading information, and reading list.
Responses will be accepted until selection is complete.
Strong preference will be given to course developers interested in continuing their association with the School of Information by applying to teach the developed course as a lecturer. The separate lecturer application can be found here: https://aprecruit.berkeley.edu/JPF04944.
Compensation
Compensation for course development will be offered via vendor payment from UC Berkeley. To be eligible to receive compensation, the successful proposer will need to register with the UC Berkeley Accounts Payable Vendoring Team and must meet all applicable university requirements. Our expert team will walk you through the process to ensure that your vendor profile is active before work proceeds. This is not a visa opportunity.
The University of California, Berkeley is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status. For the complete University of California nondiscrimination and affirmative action policy, see: http://policy.ucop.edu/doc/4000376/NondiscrimAffirmAct
Questions
Questions about this call for proposals can be directed to Amanda Gill, MIDS Academic Program Director.
