ML Engineering for the Generative AI Era

A 10-week applied program focused on the data and engineering skills needed to build generative AI systems — data pipelines, RAG, function calling, lightweight fine-tuning, AI agents, and end-to-end evaluation.

Tuition: $8,500 · Duration: 10 weeks · Orientation: April 30, 2026 · Course Start: May 5, 2026

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Who This Program Is For

  • Senior undergraduate students preparing for applied AI engineering roles
  • Technicians and engineers supporting AI-enabled operations
  • Employers building internal AI capability

Weekly Format

  • Live lecture (1.5 hrs)
  • Hands-on programming lecture (1.5 hrs)
  • Office hours (1.5 hrs)
  • Weekly homework (~5 hrs)
  • Independent project work (~3.5–5.5 hrs)

Total commitment: ~13–15 hours per week

Prerequisites

  • Basic programming concepts
  • Familiarity with data structures & algorithms
  • Introductory machine learning knowledge

Program Goals

  • Build the technical foundation needed for AI engineering and data engineering roles
  • Prepare students to work with data pipelines that support LLMs and generative AI systems
  • Develop applied experience with modern AI tools, model workflows, and evaluation practices
  • Create a structured career pipeline for high-demand roles in AI systems

Learning Outcomes

  • Explain fundamentals of LLMs and the role of data in AI scaling
  • Collect, extract, clean, label, and prepare data for generative AI
  • Implement scalable data processing methods
  • Use synthetic data and human data workflows
  • Work with text, image, audio, and video pipelines
  • Build retrieval-augmented generation (RAG) systems
  • Apply function calling, orchestration, and evaluation methods
  • Complete lightweight fine-tuning and summarization workflows
  • Design and present an end-to-end capstone AI system

10-Week Course Schedule

Week 1 — Foundations and Tooling: Generative AI landscape, LLM fundamentals, dev environments, data's role in AI scaling.
Week 2 — Agent Workflows: AI agents, task decomposition, tool use, prompt patterns, human data workflows.
Week 3 — OCR, ASR, and TTS: Multimodal data extraction, OCR, speech-to-text, text-to-speech.
Week 4 — RAG Foundations: Retrieval-augmented generation, embeddings, vector databases, chunking strategies.
Week 5 — Hybrid Search & Data Quality: Keyword search, semantic search, hybrid retrieval, filtering, visualization.
Week 6 — Function Calling: Structured outputs, tool/function calling, schema design, reliability patterns.
Week 7 — Orchestration & Evals: Workflow orchestration, evaluation design, model comparison, human-in-the-loop review.
Week 8 — Fine-Tuning & Summarization: Lightweight fine-tuning, synthetic data generation, summarization pipelines.
Week 9 — Scaling AI Workflows: Data pipelines, databases, monitoring, cost/performance tradeoffs.
Week 10 — Capstone: End-to-end AI system design, integration, presentation, reflection.

Assessment Breakdown

Weekly homework & programming assignments40%
Applied labs and participation20%
Model / data evaluation exercises15%
Final capstone project25%

Capstone Project

The capstone is an end-to-end AI system that integrates the major skills from the course. Each project includes a defined user problem, a data collection or extraction process, data cleaning/filtering/annotation, a model workflow (RAG, function calling, fine-tuning, or summarization), a clear evaluation plan, and a final presentation or demonstration.

Recommended Tools & Technologies

Python and common data libraries; APIs for large language models and generative AI systems; data storage and database tools; embedding and vector search tools; annotation and evaluation workflows; and development tools for reproducible projects.

Enrollment & Next Steps

Get in touch about start dates, financial aid, and employer partnerships.

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