RFP: Course Development, Online Course in Machine Learning at Scale
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 at Scale.
About the Course
This 14-week master’s-level online course explores the design and implementation of large-scale machine learning systems using distributed computing frameworks. Emphasizing Apache Spark and PySpark, the course should provide practical experience building and deploying scalable ML pipelines, applying GPU-accelerated training for large language models (LLMs), and managing modern data architectures using Delta tables. The course should aim to bridge academic theory and applied industrial practice in big data machine learning.
The instructor should assume that students are well-advanced in data science or related fields, with prior coursework in statistics or machine learning, proficient in Python, and familiar with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch). Students are expected to be self-motivated and capable of hands-on experimentation in distributed environments.
The core topics should cover foundations of distributed computing, including the MapReduce paradigm, an overview of Hadoop, Spark (RDDs, DataFrames), and Apache Spark, as well as Delta Tables. They should include discussions on transformations, actions, lazy evaluation, data ingestion, and schema evolution.
The machine learning pipelines at scale focus should include end-to-end ML pipelines with Spark MLlib and PySpark, as well as deployment in a cloud environment. Other topics should include:
- scalable algorithms and hyperparameter optimization,
- distributed gradient descent,
- decision trees,
- clustering, and
- Optuna/Ray Tune integration.
Other topics that should be considered include:
- Time series and streaming data (e.g., Windowing, forecasting (ARIMA, Prophet), and real-time analytics in Spark),
- LLM training and distributed inference,
- Distributed transformer fine-tuning,
- GPU acceleration, and
- Large-scale inference.
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.
