AI Workforce Development

Created May 2022

A well-developed, knowledgeable AI workforce accelerates any organization’s ability to gain the leap-ahead advantages AI promises. At the SEI, we bring the latest academic advances at Carnegie Mellon University to real world challenges faced by defense and national security organizations to advance the professional discipline of AI engineering. Through tailored interactive workshops, we share our expertise with AI teams, practitioners, and leaders.

Doing AI as Well as AI Can Be Done...At Scale

Creating, deploying, and maintaining AI solutions requires unique skillsets and mindsets, and organizations including the U.S. Department of Defense, the National Security Commission on AI, and Georgetown Center for Security and Emerging Technology have identified the shortage of AI talent as a challenge to creating reliable AI solutions.

As the SEI leads the development of a community to accelerate the discipline of AI engineering, we are surfacing the needs of organizations in not only creating AI mission solutions but also approaching the use of AI from an engineering point of view to enable teams to create reliable AI solutions again and again: How do you create human-centered, scalable, robust, and secure AI solutions? How do you know if AI is right for your problem? How do teams implement ethical AI principles? Who do we need on AI teams?

Tailored Learning for Teams

The SEI has developed several workshops for teams at the request of organizations in a variety of sectors. These workshops can be tailored to your needs and mission challenges, and most can be delivered in formats that range from half a day to a week. Contact us to bring our experts to your team or to request a workshop on a topic not listed here.

Introduction to AI Engineering
What does it take to create AI systems that are human-centered, robust and secure, and scalable? Drawing on case studies from the Department of Defense and industry, instructors will introduce frameworks and resources for how to design, develop, deploy, and maintain transformative and trustworthy AI. The course covers the lifecycle of an engineering project to provide students with an example of what it takes to build an AI system from a business case and deploy it to a real-world setting. This three-day foundational course is designed to deliver cross-functional knowledge for engaging in AI engineering projects. Students from a variety of backgrounds and experiences with a common interest in AI engineering would benefit from attending.
Problem Framing for AI
How do you know if AI is right for your problem? What outcome are you working toward? This workshop equips teams to ask questions that drill into the root cause of problems, to foster empathy for problem stakeholders, to understand where and how technology fits in, and to ultimately achieve innovative outcomes that leverage AI systems.
Where to Start with AI Ethics
Ethical principles for AI abound, and still teams often have difficulty putting idealized principles into action. In this workshop, you’ll learn how to implement AI ethics, tools and practices to get your team to coalesce around shared goals.
Essential Skillsets & Mindsets for Data Technicians
Data is a key factor in creating and deploying AI solutions that are implementable. What do you look for in hiring team members who will procure, prepare, cleanse, and model your data? Leaders and managers of AI teams and projects will learn how to go beyond lists of academic or technical qualifications to spot the perspectives they need to steward the data that drives their AI solutions.
Data and Tactical ML Pipelines
Data ingestion, cleansing, protection, monitoring, and validation are necessary for engineering a successful AI system—and they require tremendous amounts of resources, time, and attention. This workshop provides technicians with an introduction to understanding of the importance of data, the flow of data to an application, and how data pipelines effect models.