Industrial AI Application and Practice
Industrial AI deployment in 2026 is no longer only a matter of shipping a trained model: enterprises increasingly ship tool-calling agents that read live systems, act on them, and are themselves built with AI coding agents, so the professional work shifts to specification, guardrails, evaluation harnesses, observability, and accountability for machine-produced output. The course therefore keeps its MLOps and enterprise-deployment spine and extends it to agentic architecture, retrieval over enterprise data, cost and latency budgets, human-in-the-loop escalation, and post-deployment drift monitoring.
Current description → proposed description
This course bridges the gap between AI theory and real-world deployment by immersing students in the practical application of Artificial Intelligence across industrial sectors such as manufacturing, healthcare, logistics, finance, and agriculture. Students will analyze case studies, evaluate deployment architectures, and work with real or simulated datasets to implement AI solutions that meet performance, security, and compliance requirements in enterprise environments. Topics include production-grade model development, MLOps workflows, edge-AI integration, ethical and regulatory considerations, model monitoring and retraining, and the use of APIs and cloud platforms for scalable deployment. Emphasis is placed on building AI systems that are robust, explainable, and aligned with organizational goals. By the end of the course, students will have delivered an AI project proposal and prototype aligned with an industry-specific use case, preparing them for roles in AI consulting, system integration, and applied research.
This course bridges the gap between AI theory and real-world deployment by immersing students in the practical application of Artificial Intelligence across industrial sectors such as manufacturing, healthcare, logistics, finance, and agriculture. Students will analyze case studies, evaluate deployment architectures, and work with real or simulated datasets to implement AI solutions that meet performance, security, and compliance requirements in enterprise environments. Topics include production-grade model development, MLOps workflows, edge-AI integration, ethical and regulatory considerations, model monitoring and retraining, and the use of APIs and cloud platforms for scalable deployment. The course extends these practices to the agentic systems now entering industrial production: retrieval-augmented generation over enterprise data, tool and function calling through interfaces such as the Model Context Protocol, agent evaluation harnesses and LLM-as-judge review, guardrails against prompt injection and tool supply-chain compromise, agent observability, cost and latency budgeting, and human-in-the-loop escalation. Students also take professional accountability for work produced by AI coding agents, documenting model and data provenance and monitoring for post-deployment drift. Emphasis is placed on building AI systems that are robust, explainable, and aligned with organizational goals. By the end of the course, students will have delivered an AI project proposal and prototype aligned with an industry-specific use case, preparing them for roles in AI consulting, system integration, and applied research.
What changes
- Agentic orchestration: tool/function calling and Model Context Protocol servers added to the deployment stack
- Retrieval-augmented generation and vector indexing over enterprise data
- Agent evaluation harnesses, LLM-as-judge review, and post-deployment drift monitoring
- Guardrails for prompt injection and tool supply-chain compromise, with human-in-the-loop escalation
- Explicit cost, latency, and provenance accountability for work produced by AI coding agents
6 proposed outcomes, mapped to 12 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to differentiate defensible artificial intelligence capability claims from popular, historical, and vendor framings of AI when appraising candidate industrial use cases in sectors such as manufacturing, healthcare, logistics, finance, and agriculture, grounding the choice of technique class (probabilistic, search-based, learned, or agentic) in the data, latency, and accuracy the problem can actually supply.
Separating marketing and popular usage of "AI" from what a system semantically and historically is, before a use case is committed to, is exactly PLO 5.2's defining-and-evaluating work, while selecting among probabilistic models, search, neural approaches, and agentic designs requires the foundational AI theory and mathematics named in PLO 3.1.
Students will be able to construct an enterprise data acquisition and management pipeline that collects, cleans, stores, and indexes data from production APIs, operational databases, and edge or sensor streams, including the embedding and vector-index layer on which retrieval-augmented industrial applications depend, with documented provenance, lineage, retention, and data-quality gates, demonstrating the pipeline's fitness for purpose by analyzing the acquired data with appropriate statistical and machine-learning methods against the targeted industrial use case.
PLO 2.1 asks for collecting, cleaning, storing, and analyzing data from multiple sources using APIs, databases, and machine learning algorithms to solve a real-world problem, and this CLO carries both halves — the multi-source acquisition and storage pipeline and the statistical and machine-learning analysis that proves the data can support the industrial use case — while the embedding plus API-served retrieval layer it also requires is the industrial LLM deployment substrate specified in PLO 6.1.
Students will be able to design a domain-specific, tool-calling AI agent that executes real tasks against enterprise systems through function calling and Model Context Protocol servers, specifying and refining its instructions and prompt contracts against measured alignment, output-format conformance, and adaptability across varied enterprise inputs, and setting least-privilege tool scopes, guardrails against prompt injection and tool supply-chain compromise, observability, and thresholds for human-in-the-loop escalation.
Designing and integrating an agent for secure, flexible task execution with real-time knowledge integration is PLO 2.2 stated directly, and the required before-and-after refinement of the agent's instructions and prompt contracts against measured alignment, output-format conformance, and adaptability across varied enterprise inputs is precisely the "better alignment, formatting, and adaptability" evidence PLO 4.2 demands.
Students will be able to implement a production-grade industrial AI prototype that combines learned models built with supervised, unsupervised, or reinforcement learning with agentic orchestration, deploying it through an MLOps workflow across cloud and edge targets under explicit cost, latency, and security budgets, and refining it from evaluation-harness, LLM-as-judge, and drift diagnostics.
Building the system from supervised, unsupervised, or reinforcement learning together with intelligent agents is PLO 4.1; the structured cloud, enterprise, and edge deployment through APIs under operating budgets is PLO 6.1; and refining the prototype in response to evaluation-harness, judge, and drift diagnostics is the diagnostic-driven iteration required by PLO 6.2.
Students will be able to critique a proposed industrial deployment for bias, fairness, transparency, privacy, accountability, regulatory exposure, and economic access, and for the harm borne by the workers, patients, or customers it touches, justifying the safeguards, disclosures, and lines of human accountability it adopts as an expression of a Christian vocational commitment to serving the neighbor.
The bias, fairness, transparency, privacy, accountability, economic access, and harm analysis in this CLO is the ethical and legal integrity outcome that the workbook states twice in identical words and with identical I/D/AE rows, once under Christian Faith as PLO 1.1 and once under Integrated Disciplinary Knowledge as PLO 3.2, so the CLO is mapped to both; the requirement to justify safeguards as service to the neighbor, with professional humility about who bears the risk, additionally carries the vocational articulation of PLO 1.2.
Students will be able to communicate an industry-specific AI project proposal and prototype, including its architecture, evaluation evidence, known failure modes, residual risk, and post-deployment monitoring commitments, to both technical reviewers and non-technical organizational stakeholders.
Presenting architecture, results, and ethical and risk considerations to technical and non-technical audiences with clarity and responsibility is PLO 5.1 verbatim, and the requirement to report evaluation metrics alongside known failure modes and residual error is the performance-and-limitations assessment half of PLO 6.2.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all twelve.
The workbook maps this course too
The proposal workbook's assessment map already assigns program learning outcomes to this course. The outcomes above were written to cover it.
| Program outcome | Workbook level | In this draft |
|---|---|---|
| PLO 1.1 Ethical and Legal Integrity in AI | D,AE | covered |
| PLO 1.2 Vocation and Christian Worldview | I | covered |
| PLO 2.1 Data Acquisition for Real Problems | D,AE | covered |
| PLO 2.2 Domain-Specific AI Agents | D,AE | covered |
| PLO 3.1 AI Theory and Mathematics | I | covered |
| PLO 3.2 Ethical and Legal Integrity in AI | D,AE | covered |
| PLO 4.1 Learning-Based Systems and Agents | D,AE | covered |
| PLO 4.2 Prompt Engineering for LLMs | D,AE | covered |
| PLO 5.1 Communicating AI to Any Audience | D,AE | covered |
| PLO 5.2 Defining and Evaluating AI | I | covered |
| PLO 6.1 LLMs in Industrial Settings | D,AE | covered |
| PLO 6.2 Model Evaluation and Refinement | D,AE | covered |
I = Introduced · D = Developed · AE = Assessed at Exit