{"id":174567,"date":"2026-05-27T09:00:23","date_gmt":"2026-05-27T13:00:23","guid":{"rendered":"https:\/\/www.bu.edu\/eng\/?p=174567"},"modified":"2026-05-26T20:07:21","modified_gmt":"2026-05-27T00:07:21","slug":"building-scalable-ai-in-an-ai-software-engineering-masters","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/eng\/2026\/05\/27\/building-scalable-ai-in-an-ai-software-engineering-masters\/","title":{"rendered":"How the AI in Software Engineering Curriculum Prepares Engineers for Production-Scale AI"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">It\u2019s become undeniable that artificial intelligence (AI) has <\/span><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2773207X24001386\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">permeated nearly every industry<\/span><\/a><span style=\"font-weight: 400;\"> imaginable\u00a0\u2014 including software engineering. Yet, when the focus lies\u00a0<\/span><i><span style=\"font-weight: 400;\">too <\/span><\/i><span style=\"font-weight: 400;\">much\u00a0on model production and not enough on scalability, projects inevitably suffer.\u00a0<\/span><span style=\"font-weight: 400;\"><\/span><span style=\"font-weight: 400;\">At Boston University (BU), our <\/span><a href=\"http:\/\/www.bu.edu\/eng\/academics\/explore-degree-programs\/online-master-of-science-in-software-engineering-for-artificial-intelligence\/\"><span style=\"font-weight: 400;\">online Master of Science (MS) in Software Engineering for Artificial Intelligence<\/span><\/a><span style=\"font-weight: 400;\"> aims to address this problem by preparing students for the realities of deploying and maintaining AI models at scale.\u00a0<\/span><\/p>\n<h2><b>Why Production-Scale AI Requires More Than Model Building<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Successful production-scale AI doesn\u2019t mean getting a model to work just once. It\u2019s about integrating AI into real software systems that are scalable, reliable, secure, and usable. This ability to deploy AI models in the real world goes much deeper than model-building, requiring an extensive understanding of production-grade software systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Rather than framing our online <\/span><a href=\"http:\/\/www.bu.edu\/eng\/academics\/explore-degree-programs\/online-master-of-science-in-software-engineering-for-artificial-intelligence\/\"><span style=\"font-weight: 400;\">Master of Science in Software Engineering for AI<\/span><\/a><span style=\"font-weight: 400;\"> program around data science, software engineering, and AI as disparate topics, at Boston University, we aim to ensure students are equipped to integrate models into actual, everyday systems.<\/span><\/p>\n<h3><b>Production AI Lives Inside Software Systems, Not Standalone Demos<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Today, AI systems used in growing organizations must operate within larger and more complex application environments, data pipelines, release workflows, and user-facing systems. When software engineers and AI teams are too focused on model-building, then, they fail to account for the nuances involved in integrating models with existing systems\u00a0\u2014 which can lead to failure.<\/span><\/p>\n<h3><b>Reliability, Governance, and Scale Are Engineering Problems<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Even once an AI model is deployed, ongoing governance, reliability, and scaling issues remain the responsibility of the AI software engineer. At this point, the work becomes operational. Engineering teams must ensure guardrails are in place for <\/span><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9018249\/4\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">compliance and governance purposes<\/span><\/a><span style=\"font-weight: 400;\"> and that systems can handle fluctuating volumes of requests without affecting performance.\u00a0<\/span><\/p>\n<h2><b>How the Online Master&#8217;s in Software Engineering for AI Curriculum Is Structured<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">At BU, the online master&#8217;s in software engineering for artificial intelligence prepares students for the inherent challenges and opportunities of producing scalable AI in the modern world. Its <\/span><a href=\"http:\/\/www.bu.edu\/eng\/admissions\/graduate\/omse-curriculum\/\"><span style=\"font-weight: 400;\">30-credit curriculum<\/span><\/a><span style=\"font-weight: 400;\"> is organized into a:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pre-program phase<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Software engineering phase<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI core<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Year-long capstone<\/span><\/li>\n<\/ul>\n<h3><b>Pre-Program Foundations That Prepare Students to Build<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Before delving into the bulk of the program, students begin with orientation and a software engineering and data science bootcamp. The bootcamp ensures that incoming learners are on the same level when it comes to foundational skills, preparing them for the program\u2019s more advanced coursework. This may be especially valuable for working professionals coming from adjacent technical backgrounds, as it provides a structural framework on programming, tools, and data fundamentals before progressing into core coursework.<\/span><\/p>\n<h3><b>Core Coursework That Builds Toward Production AI<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Upon completing this foundational phase, students move into Year One, which is broken up into modules such as:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Software Engineering Fundamentals<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/isjem.com\/download\/the-rise-of-python-how-this-language-is-shaping-the-future-of-tech\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Programming<\/span><\/a><span style=\"font-weight: 400;\"> Toolkit for Data Science<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Algorithms for Scalable Systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Software Engineering at Scale<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By Year Two (semesters three and four), students complete modules including:<\/span><span style=\"font-weight: 400;\"><\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI\/LLM-Aided Software Development<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Human Centric\u00a0AI UX<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Design and Distribution at Scale, AI\/ML Ops<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Responsible and Ethical Data Science and AI<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">As part of the program&#8217;s second year, students also complete a capstone project\u00a0that involves designing and deploying a production-grade, <\/span><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0040162523005632\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AI-enabled application at scale<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><b>Building the Foundations for Scalable AI Systems<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">The early coursework of the online MS in Software Engineering for AI program intends to establish the strong technical base needed for later production challenges. Topics encompass:\u00a0<\/span><\/p>\n<h3><b>Data Algorithms for Scalable Systems<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Production AI systems depend on efficient data processing, algorithmic thinking, and performance under scale. This course specifically prepares students to design AI models that can be deployed seamlessly even in the most complex of environments.<\/span><\/p>\n<h3><b>Software Engineering Fundamentals<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">At their very core, all AI systems require sound software engineering practices. From basic architecture and maintainability to testing and beyond, AI software engineering courses set a solid foundation for disciplined development.<\/span><\/p>\n<h3><b>Programming Toolkit for Data Science<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Production AI work depends on fluency with modern programming tools for data-driven systems that extend beyond abstract concepts. In this course specifically, students build proficiency in the tools they&#8217;ll be using in the real-world, including:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pandas<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">NumPy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Matplotlib<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Learning these tools, along with using them to complete a final project, helps bridge between software engineering and machine learning (ML) workflows more seamlessly.<\/span><\/p>\n<h2><b>Learning How Models Become Real Systems<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Part of what sets the Boston University software engineering for artificial intelligence program apart is that it centers on this reality: Production-scale AI calls for more than just understanding models\u00a0but rather a deep understanding of how they become part of reliable systems.<\/span><\/p>\n<h3><b>Machine Learning Fundamentals as the Starting Point, Not the Finish Line<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Machine learning fundamentals matter, but they&#8217;re only one part of preparing for AI model deployment. In order to create and deploy AI scalability solutions, software engineers need extensive ML training and experience to <\/span><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0164121223003023\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">build and evaluate systems responsibly<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is why our program includes dedicated topics covering machine learning fundamentals alongside software engineering and scalable systems coursework.<\/span><\/p>\n<h3><b>Software Engineering at Scale<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">AI reliability and scalability also require an extensive knowledge of software engineering fundamentals. In this degree program, students prepare for production AI by exploring coursework that confronts the challenges of achieving AI-powered scalability and helps them think critically about:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Architecture<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Load<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Performance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Failure models<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Maintainability in distributed or high-demand environments<\/span><\/li>\n<\/ul>\n<h2><b>Preparing for AI Model Deployment and Operationalization<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Moving AI from experimentation into production means being able to handle everything from deployment and release workflows to infrastructure and operational AI monitoring. This is a core component of the software engineering for artificial intelligence program; students explore coursework in AI\/LLM-aided software development and data design\/distribution at scale.<\/span><\/p>\n<h3><b>AI\/LLM-Aided Software Development<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">It has become increasingly common for production engineers to work with <\/span><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3715003\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">AI-assisted development tools<\/span><\/a><span style=\"font-weight: 400;\">. However, this has not negated the need for engineers to effectively evaluate output quality, correctness, security, and maintainability. If anything, these skills are even more critical in preparing for successful deployment at scale.<\/span><\/p>\n<h3><b>Data Design and Distribution at Scale, AI\/ML Ops<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Even post-launch, production AI depends on data design and distribution knowledge to support systems. In the BU program, students complete coursework in adjacent topics like:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI model deployment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI monitoring<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scalable AI<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Model operations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pipeline design<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data architecture<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Integrating hands-on practice, this coursework prepares engineers for the inherent challenges of deploying and supporting systems at scale.<\/span><\/p>\n<h2><b>Reliability and Monitoring in Production AI<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">BU&#8217;s online master&#8217;s in software engineering for AI program also supports AI reliability and monitoring through coursework in MLOps, scalable systems, release workflows, and responsible AI practices.\u00a0<\/span><\/p>\n<h3><b>Why Monitoring Matters After Deployment<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">After AI models are deployed, engineers still need to <\/span><a href=\"https:\/\/www.adalovelaceinstitute.org\/blog\/post-deployment-monitoring-of-ai\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">observe and manage them<\/span><\/a><span style=\"font-weight: 400;\"> because the conditions under which they&#8217;re deployed may change. With that, usage patterns may shift, and failure can show up in production. Our program&#8217;s emphasis on production-style workflows, MLOps, and end-to-end applications at scale prepares students to keep systems running effectively and reliably after launch.<\/span><\/p>\n<h3><b>Reliability Comes From Engineering Discipline, Not Hope<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Additionally, the program curriculum reinforces dependable engineering behavior across multiple courses instead of simply touching on it as a standalone subject. Thus, students learn how to carry out the testing, review, pipeline design, release workflows, and operational oversight needed to support long-term model reliability.\u00a0<\/span><\/p>\n<h2><b>Governance, Ethics, and Human-Centered Design in AI Systems<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">In addition to being scalable and reliable, production AI needs to be trustworthy, explainable, usable, and governed well. This is another area where Boston University\u2019s program really stands out in equipping students for real-world work.<\/span><\/p>\n<h3><b>Responsible and Ethical Data Science and AI<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">In the Responsible and Ethical Data Science and AI module, students explore the ethical frameworks that should be applied to all data-driven systems as well as the role of generative AI in misinformation. This coursework underscores that <\/span><a href=\"https:\/\/www.researchgate.net\/publication\/377701616_Ethical_Considerations_in_AI_and_Data_Science_Bias_Fairness_and_Accountability\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">responsible production AI<\/span><\/a><span style=\"font-weight: 400;\"> requires: <\/span><span style=\"font-weight: 400;\"><\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Attention to ethics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI governance monitoring<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Oversight<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Explainability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Long-term accountability<\/span><\/li>\n<\/ul>\n<h3><b>Human-Centric AI UX<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Meanwhile, in the Human-Centric AI UX module, students learn to build systems that are usable, interpretable, and effective in real settings. This prepares them to create AI models that work for real users\u00a0\u2014 beyond the sole purpose of passing technical benchmarks.<\/span><\/p>\n<h2><b>How the BU Curriculum Builds Toward End-to-End Production Thinking<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Our <\/span><a href=\"http:\/\/www.bu.edu\/eng\/academics\/explore-degree-programs\/online-master-of-science-in-software-engineering-for-artificial-intelligence\/\"><span style=\"font-weight: 400;\">online Master&#8217;s in Software Engineering for AI<\/span><\/a><span style=\"font-weight: 400;\"> degree program offers a meticulously curated curriculum that reflects the realities and opportunities of this dynamic field\u00a0\u2014 ultimately designed to prepare students for production-grade AI system operation.<\/span><\/p>\n<h3><b>From Foundations to Full-System Integration<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The integrated nature of the program allows students to move progressively from algorithms and software engineering basics to more complex scalability, monitoring, governance, and user-centered design concepts. By building on foundational skills first, students learn to master end-to-end production thinking over time.<\/span><\/p>\n<h3><b>Why Production AI Requires Breadth Across Courses<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">While no single course in the program teaches &#8220;production AI&#8221; alone, this concept is repeatedly explored throughout. With a curriculum covering a combination of scalable systems data design, deployment workflows, and monitoring thinking, students build breadth across courses while learning how to design and operate AI systems successfully for production.<\/span><\/p>\n<h2><b>The Capstone as a Production-Scale Proving Ground<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">In this master\u2019s program, the capstone is the culminating project that ties everything together\u00a0\u2014 giving students a unique opportunity to oversee a project from start to finish.<\/span><\/p>\n<h3><b>Designing and Deploying an End-to-End AI-Enabled Application at Scale<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">As opposed to learning in isolation the theory surrounding AI software engineering, students are expected to design and deploy a full AI-enabled system at scale. Here, they can apply what they&#8217;ve learned in every module and put newfound skills to use.<\/span><\/p>\n<h3><b>Why a Year-Long Capstone Matters for Production Readiness<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The rigor of a year-long capstone project also gives students the opportunity to integrate concepts of software engineering, AI, deployment, operations, and governance in one applied project. This, in turn, may better prepare them for sustained integration work that production-scale engineers are expected to tackle in real-world settings.\u00a0<\/span><\/p>\n<h2><b>What This Means for Students Who Want to Become AI Software Engineers<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">For those interested in a career in AI software engineering, our program:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Prepares students directly for AI software engineering roles<\/b><span style=\"font-weight: 400;\">\u00a0where AI, software deployment, and architecture intersect. This is achieved through extensive coursework that cultivates the practical skills needed in positions like AI Software Engineer, MLOps Engineer, Platform Engineer, and Software Architect.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Builds skills that translate to real engineering teams<\/b><span style=\"font-weight: 400;\">\u00a0through industry-relevant tools, real-world data sets, production-style workflows, cloud-based deployment, and a collaborative online learning setting.<\/span><\/li>\n<\/ul>\n<h2><b>Take the Next Step Toward Building Production-Scale AI Systems at BU<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Looking to learn more about the <\/span><a href=\"http:\/\/www.bu.edu\/eng\/academics\/explore-degree-programs\/online-master-of-science-in-software-engineering-for-artificial-intelligence\/\"><span style=\"font-weight: 400;\">online Master of Science in Software Engineering for Artificial Intelligence<\/span><\/a><span style=\"font-weight: 400;\"> at Boston University? Review the <\/span><a href=\"http:\/\/www.bu.edu\/eng\/admissions\/graduate\/omse-curriculum\/\"><span style=\"font-weight: 400;\">curriculum and course sequence<\/span><\/a><span style=\"font-weight: 400;\"> for yourself and consider whether this program may align with your interests and career goals. Get in touch to request more information or get started with your <\/span><a href=\"https:\/\/bu-eng.cas.myliaison.com\/applicant-ux\/#\/login\"><span style=\"font-weight: 400;\">application<\/span><\/a><span style=\"font-weight: 400;\"> for admission today.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>It\u2019s become undeniable that artificial intelligence (AI) has permeated nearly every industry imaginable\u00a0\u2014 including software engineering. Yet, when the focus lies\u00a0too much\u00a0on model production and not enough on scalability, projects inevitably suffer.\u00a0At Boston University (BU), our online Master of Science (MS) in Software Engineering for Artificial Intelligence aims to address this problem by preparing students [&hellip;]<\/p>\n","protected":false},"author":25697,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1429],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/174567"}],"collection":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/users\/25697"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/comments?post=174567"}],"version-history":[{"count":2,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/174567\/revisions"}],"predecessor-version":[{"id":174571,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/174567\/revisions\/174571"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/media?parent=174567"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/categories?post=174567"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/tags?post=174567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}