HawkAI for Developers of AI Tools and Applications

HawkAI-Inforgraphics-Level-3

Level-3 HawkAI Courses and Enrollment Requirements

  • HawkAI Level 3 short courses will benefit those who aspire to become AI researchers and AI-tool developers – computational knowledge and relevant background is a pre-requisite. 
  • Enrolling in this course sequence thus requires working proficiency with code development, Linux, and computational project development experience (outside AI).  
  • Enrollment is limited to 40 attendees, wait list will be maintained.
  • Note the availability of preparation-level "OneIT" courses (see below). 
    • Content of these courses may serve as a hint of what level of computational proficiency is expected from those who plan to enroll in HawkAI Level 3 ... 
    • ... while being excellent refreshers, the OneIT courses are not a substitute for the expected formal training in software development and cannot serve as the only preparation for HawkAI Level 3.
  • The required HawkAI short courses listed here cannot be taken individually - the attendees must register in all required courses (one combined enrollment) plus register for at least two of the electives (individual course enrollment).
    • All five (5) required courses must be attended to be eligible for HawkAI Level 3 certificate.
    • Enrolled attendees must also enroll in at least 2 elective short courses.
    • Those enrolled in Level-3 sequence can (and are encouraged to) enroll in any number >2 of elective courses.
  • To obtain Level 3 Certificate - seven (7) courses are required:
    - 301, 302, 303, 304, 310 courses are required
    - at least two (2) of the five (5) 305-309 courses are required
  • To obtain Level-3 certificate, holding Level-1 and/ or Level-2 certificates is NOT a pre-requisite as Level-3 serves a different group of attendees.
    • Note however, that Level-1 and Level-2 experience and knowledge will be expected to gain full benefits from Level-3 courses.
  • Each Level-3 short course will typically be 2 hours in duration - however, to become really proficient in the field, attendees will be encouraged to spend additional time at self-directed reinforcement of the learned skills, which may require notable additional time investment.

Level-3 Course Structure and Calendar

Enroll Now - Fall 2025 - Enrollment links will open on June 1, 2025

  • Self-assessment of your computational proficiency
    • HawkAI Level 3 requires computational proficiency ... please perform this self-assessment (click here)
    • Enroll only if - based on the self-assessment - you are comfortable that you meet the expected computational proficiency expectations

  • Enrollment is a two-step process:

    Step 1: Enroll in this set of required courses:

Enroll in all 5 required HawkAI Level 3 courses - 301, 302, 303, 304, 310 

       Step 2: Enroll in at least 2 of the elective courses:

Enroll HawkAI-305 Enroll HawkAI-306 Enroll HawkAI-307 Enroll HawkAI-308 Enroll HawkAI-309

Level-3 Preparation Courses offered by OneIT - Calendar, Description, Enrollment

  • If needed ... This is a great opportunity to refresh your knowledge prior to enrolling in Level 3 HawkAI sequence
  • Preparation courses include:
    • HawkAI-ITS-1: Python for Data Analysis: Python Fundamentals (with Pandas)  

    • HawkAI-ITS-2: Python for Data Analysis: Machine Learning Using Scikit-Learn with Python  

    • HawkAI-ITS-3: Basic Linux Commands on Clusters  

    • HawkAI-ITS-4: Introduction to High Performance Computing (Using Argon) 

    • HawkAI-ITS-5: Programming for AI/ML – Coding with AI 

    • HawkAI-ITS-6: Fundamentals of Cloud Computing with MS Azure  

  • OneIT Preparation course descriptions, course offering format and dates, enrollment links

HawkAI Courses ... Who Developed, Who Teaches

Content Outlines of Level-3 HawkAI Short Courses

  • HawkAI 301: Introduction to AI Design and Development

    • This short course focuses on the big‑picture work of getting an AI tool into production.  It explores the practical depth that tech‑savvy engineers and researchers need. Beyond scoping, feasibility checks, and resource mapping, we frame five competency areas: Technical foundations, Cross‑disciplinary collaboration, Product & user‑centric perspective, Computational Resources, and Real‑world case studies.

    • Participants leave with a vetted AI design plan, the vocabulary to collaborate across fields, and a roadmap for launching maintainable, compliant AI solutions.
  • HawkAI 302: Ethics of AI for Developers
    • This course aims to help participants navigate the complex ethical landscape of AI from the perspective of AI developers. This course moves beyond an introductory level understanding of trustworthiness in AI to explore issues of bias, validity, security, fairness, privacy, transparency, explainability, and regulation related to AI development. It offers an integrated application of ethical principles so AI developers can make more informed decisions about AI development based on ethical considerations related to truthfulness, usability, beneficence, respect for persons, justice, and responsibility.
  • HawkAI 303: Data Management for AI/ML Development
    • This hands-on short course will focus on the ways to search for, collect, prepare, curate, and store data necessary for AI/ML development, including:
      • Data access/moving/storing/making available for training and analysis - data originating from a variety of sources, including public databases, local-lab collected data, data from UIHC, as well as synthetically generated data.
      • Locations available for storage and computational access of such data will be discussed and their differences explained, including that of cloud resources.
    • The course will demonstrate several case studies of AI/ML development that were properly managed and/or were challenging from the data management perspective.
  • HawkAI 304: Design and Development of AI Methods and Tools
    • This course will cover exemplar tools, approaches, methods, frameworks, interfaces, and performance assessments common to many AI/ML development efforts. Emphasizing best practices ensures the success of a production-ready product or research objective that employs AI/ML techniques.     
  •  HawkAI 305: Specifics of Generative AI Development - Integrating Generative AI Into Your Applications
    • This hands-on workshop will equip developers with the essential skills for integrating Large Language Models (LLMs) and other Generative AI models into practical applications such as conversational chatbots, autonomous agents, content generators/transformers, and more. Participants will learn effective techniques for API integration, context management, and prompt engineering while building sample real-world applications during guided coding sessions. The course demonstrates how LLMs can serve as the "brain" for various tools including document analyzers, recommendation engines, and creative assistants that can be immediately applied to participants' own projects.
  • HawkAI 306: Specifics of Near-time Generative AI Development - Real-Time Creativity with Generative AI and LLM APIs
    • This hands-on workshop will equip developers with essential skills for building applications that leverage large language models (LLMs) in real-time or near-real-time. Participants will learn to integrate LLMs via real-time APIs to support dynamic, time-sensitive tasks such as live script adaptation; responsive, multi-modal chatbots; interactive media generation; and other applications where low-latency AI is critical.
  • HawkAI 307: Specifics of Analytical AI: General Data
    • Analytical AI uses AI/machine learning to analyze data, identify patterns and generate insights. This course focuses on applications of analytical AI with general datasets. Emphasis is placed on data pre-processing, model development, and predictive analytics to derive insights from data.
  • HawkAI 308: Specifics of Analytical AI: Textual Data
    • This short course will focus on AI techniques including large language models (LLMs) for analyzing textual data. Topics include text preprocessing, feature extraction, classification models, and evaluation. Through hands-on exercises and case studies, participants will learn how to develop analytical AI techniques that can work with noisy, high-dimensional, and context-dependent textual inputs.
  • HawkAI 309: Specifics of Analytical AI: Image Data
    • This course will present the methods, approaches, and tools to analyze complex imaging data.  Using common Python analysis frameworks, we will provide examples of image data augmentation, image analysis frameworks, and model performance assessments. 
  • HawkAI 310: Deployment of AI/ML Tools
    • This short course explores practical paths to production-use of AI as we unpack three real-life deployments: a WebAssembly optical-grading engine that runs on classroom Chromebooks, a ResNet distilled into lean C++ for real-time eye tracking on ARM hardware, and Flask + container workflows that expose PyTorch vision and compact LLM models as web services. Along the way we'll compare edge, embedded, and cloud patterns while highlighting the trade-offs that drive each choice. We will also cover IP-protection tactics including server-side inference, scrambled weights, and hardware security keys to keep your models safe once they deploy.