Making successful use of cloud computing requires nuanced approaches to both system design and deployment methodology, involving reasoning about the elasticity, cost, and security models of cloud services. Building cloud-native applications without a firm understanding of the fundamentals of cloud engineering can leave students susceptible to cost and security pitfalls. Yet, cloud computing is not commonly taught at the undergraduate level. To address this gap, we designed an undergraduate-level course that frames cloud infrastructure deployment as a software engineering practice. Our course featured a number of hands-on assignments that gave students experience with modern, best-practice concepts and tools including infrastructure-as-code (IaC). We describe the design of the course, our experience teaching its initial offering, and provide our reflections on what worked well and potential areas for improvement. Our course material is available at https://infracourse.cloud.

We introduce a classification scheme for detecting political bias in long text content such as newspaper opinion articles. Obtaining long text data and annotations at sufficient scale for training is difficult, but it is relatively easy to extract political polarity from tweets through their authorship. We train on tweets and perform inference on articles. Universal sentence encoders and other existing methods that aim to address this domain-adaptation scenario deliver inaccurate and inconsistent predictions on articles, which we show is due to a difference in opinion concentration between tweets and articles. We propose a two-step classification scheme that uses a neutral detector trained on tweets to remove neutral sentences from articles in order to align opinion concentration and therefore improve accuracy on that domain. Our implementation is available for public use at https://knowbias.ml.