MS in Enterprise AI
The MS in Enterprise AI (EAI) is designed for professionals who are ready to move beyond “using AI tools” to building, governing, and deploying AI systems at scale. Students will learn how to engineer data pipelines, design and fine-tune LLMs, operationalize AI models, and architect AI-driven enterprise transformations.
This is a practitioner’s degree for enterprise AI implementation, designed to meet the growing need for technical leaders who can turn AI strategy into operational reality. Graduates will be able to translate between data science, engineering, and business leadership teams to build sustainable, ethical, and scalable AI systems across organizations.
Learning Outcomes
- Apply Advanced AI Techniques to Enterprise Challenges
Graduates will be able to design, build, and evaluate AI solutions using machine learning, large language models, and autonomous agents to address complex business problems across diverse enterprise contexts.
- Engineer Robust Data and Model Infrastructure
Graduates will demonstrate the ability to create scalable data pipelines, manage enterprise data using SQL, NoSQL, and vector databases, and operationalize AI models through MLOps practices such as containerization, CI/CD, and orchestration.
- Govern AI Systems Responsibly and Sustainably
Graduates will develop and implement governance frameworks that address ethics, security, explainability, and sustainability, including risk mitigation strategies for adversarial attacks, bias, and compliance requirements.
- Lead Enterprise AI Strategy and Transformation
Graduates will be able to formulate enterprise-wide AI strategies, build compelling business cases, design human-AI collaboration workflows, and apply experimental design methods to measure the impact of AI initiatives.
- Manage End-to-End AI Projects and Communicate Impact
Graduates will successfully lead AI projects from concept to deployment, integrating technical and business perspectives, and effectively communicate design decisions, risks, and outcomes to stakeholders and industry partners.
Requirements
The EAI program consists of 12 courses (30 units total) and 2 0-unit orientation modules completed prior to the start of the main program. The program is designed to be completed in four terms.
| 2 weeks | Term 1 | Term 2 | Term 3 | Term 4 |
| Completed prior to the start of the first term | CDS EA 810
Enterprise AI for Leaders 1 (1.5 units) |
CDS EA 820
Enterprise AI for Leaders 2 (1.5 units) |
CDS EA 830
Enterprise AI for Leaders 3 (1.5 units) |
CDS EA 840
Enterprise AI for Leaders 4 (1.5 units) |
| CDS EA 500
Program Orientation (0 units) |
CDS DX 602 Programming Toolkit for Data Science (3 units) | CDS EA 604 Data Engineering & Strategy for AI (3 units) |
CDS EA 710
MLOps: Managing the AI Model Lifecycle (3 units) |
CDS EA 730
Designing and Deploying Enterprise Agents (3 units) |
| CDS EA 501 Enterprise AI Bootcamp (0 units) | CDS EA 603
Foundations of Machine Learning for Enterprise Intelligence (3 units) |
CDS EA 620 Designing & Building LLM Applications (3 units)
|
CDS EA 720
AI Governance, Ethics, and Sustainability (3 units) |
CDS EA 740
AI Strategy & Business Transformation (3 units)
|
Additional Information
The EAI program offers both a fall and spring term start. The program is designed to be completed over 16 continuous months, or 4 terms. Students complete 3 courses per term (7.5 units). The program can be extended for up to 8 terms or 32 months, by only taking one core course (602, 603, 604, 620, 710, 720, 730, 740) per term while completing AI for Leaders in the first two and last two terms.
