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CMU Lecture: Machine Learning In Production / AI Engineering / Software Engineering for AI-Enabled Systems (SE4AI)
| Date | Stars |
|---|---|
| 2026-07-31 | 453 |
| 2026-08-01 | 453 |
| 2026-08-06 | 453 |
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# Machine Learning in Production (17-445/17-645/17-745) ### Fall 2022 <em>Formerly **Software Engineering for AI-Enabled Systems (SEAI)** and also taught as **AI Engineering (11-695)**, CMU course that covers how to build, deploy, assure, and maintain products with machine-learned models. Covers also **responsible AI** (safety, security, fairness, explainability) and **MLOps**. The course is crosslisted both as **Machine Learning in Production** and **AI Engineering**. For earlier offerings see websites for [Fall 2019](https://ckaestne.github.io/seai/F2019), [Summer 2020](https://ckaestne.github.io/seai/S2020), [Fall 2020](https://ckaestne.github.io/seai/F2020/), [Spring 2021](https://ckaestne.github.io/seai/S2021/) and [Spring 2022](https://ckaestne.github.io/seai/S2022/). This Fall 2022 offering is designed for students with some data science experience (e.g., has taken a machine learning course, has used sklearn) and basic programming skills, but will not expect a software engineering background (i.e., experience with testing, requirements, architecture, process, or teams is not required). Going forward we expect to offer this course at least every spring semester and possibly some fall semesters (not summer semesters).</em> --- **Note for Spring 2023: We have a fairly long waitlist on all sections for master students, but we are optimistic that we will be able to enroll most students within the first week of the semester. [Spring 2023 website](https://ckaestne.github.io/mlip-s23/)** For researchers, educators, or others interested in this topic, we share all course material, including slides and assignments, under a creative commons license on GitHub (https://github.com/ckaestne/seai/) and have also published an article describing the rationale and the initial design of this course: [Teaching Software Engineering for AI-Enabled Systems](https://arxiv.org/abs/2001.06691). A [textbook](https://ckaestne.medium.com/machine-learning-in-production-book-overview-63be62393581) is emerging. Video recordings of the Summer 2020 offering are online on the [course page](https://ckaestne.github.io/seai/S2020/#course-content). We would be happy to see this course or a similar version taught at other universities. See also an [annotated bibliography](https://github.com/ckaestne/seaibib) on research in this field. ## Course Description This is a course for those who want to build **applications** and **products** with **machine learning**. Assume you can learn a model to make predictions, what does it take to turn the model into a product and actually deploy it, have confidence in its quality, and successfully operate and maintain it? The course is designed to establish a working relationship between **software engineers** and **data scientists**: both contribute to building AI-enabled systems but have different expertise and focuses. To work together they need a mutual understanding of their roles, tasks, concerns, and goals and build a working relationship. This course is aimed at **software engineers** who want to build robust and responsible systems meeting the specific challenges of working with AI components and at **data scientists** who want to understand the requirements of the model for production use and want to facilitate getting a prototype model into production; it facilitates communication and collaboration between both roles. The course is a good fit for student looking at a career as an **ML engineer**. *The course focuses on all the steps needed to turn a model into a production system in a responsible and reliable manner.*  It covers topics such as: * **How to design for wrong predictions the model may make?** How to assure *safety* and *security* despite possible mistakes? How to design the *user interface* and the entire system to operate in the real world? * **How to reliably deploy and update models in production?** How can we *test* the entire machine learning pipeline? H
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