Break Through Tech AI fellows are undergraduate students from colleges across the country who complete Machine Learning Foundations training before joining the Studio. Their preparation includes Python, Pandas, NumPy, data cleaning, feature engineering, visualization, and machine learning model development and evaluation.

Supported by Break Through Tech coaches, fellows can analyze datasets, develop exploratory AI/ML solutions, collaborate through GitHub, and communicate technical findings to industry advisors.

Through the AI & Machine Learning Consulting Studio, fellows will:

Break Through Tech’s AI Studio pairs industry partners with two teams of undergraduate fellows to tackle a real AI or machine learning challenge over nine weeks. Using Python and a host-provided, nonconfidential dataset, fellows develop and evaluate models, analyses, or prototypes.

Break Through Tech provides coaching, while industry partners contribute a defined challenge, technical guidance, and periodic feedback. Projects must be suitable for public sharing so fellows can showcase their work on GitHub, LinkedIn, and resumes.

Depending on the approved project scope, participating organizations may receive:

Deliverables are exploratory; production deployment is outside the Studio's scope.

Participating organizations should be prepared to: 

• Provide a clearly defined AI, ML, data science, or analytics challenge
 • Share a real-world dataset, synthetic dataset, public dataset, sample dataset, or clearly framed data problem appropriate for undergraduate fellows
 • Assign an industry challenge advisor or project contact to provide business context and feedback
 • Participate in a kickoff session to introduce the organization, challenge, goals, constraints, and desired outcomes
 • Clarify any data privacy, confidentiality, security, or ethical considerations before fellows begin work
 • Support periodic check-ins, office hours, or feedback sessions during the project cycle
 • Attend final presentations, demos, or portfolio showcases when possible
 • Scope projects so they are meaningful, educational, and achievable for undergraduate fellows building AI and ML experience 

Projects should emphasize applied learning, responsible AI practice, portfolio development, and authentic industry relevance. They should not require access to highly sensitive data, production systems, or mission-critical infrastructure.