AI Con USA 2025 - DevOps

Customize your AI Con USA 2025 experience with sessions covering DevOps.

Sunday, June 8

Phil LaFrance
Coveros

GitHub Copilot Certification Bootcamp

Sunday, June 8, 2025 - 8:30am to Monday, June 9, 2025 - 5:00pm

This hands-on course helps software developers understand how to effectively incorporate Copilot into their software developer workflow.

Tuesday, June 10

Tariq King
Test IO
TA

Prompt Engineering for Software Practitioners

Tuesday, June 10, 2025 - 8:30am to 12:00pm

With the sudden rise of ChatGPT and large language models (LLMs), practitioners are using these tools for all aspects of engineering. This includes leveraging LLMs for creating software artifacts such as requirements documents, source code, and tests; reviewing them for issues and making corrective suggestions, and analyzing or summarizing results or outcomes. However, if LLM's are not fed good prompts describing the task that the AI is supposed to perform, their responses can be inaccurate and unreliable. Join Tariq King as he teaches you how to craft high-quality AI prompts and...

Jeff Payne
Coveros
TB

AI-Assisted Development: Supercharge Your Coding with GitHub Copilot and More

New
Tuesday, June 10, 2025 - 8:30am to 12:00pm

The world of software development is rapidly evolving, and AI-powered tools are leading the charge. Generative AI tools like GitHub Copilot, Cursor, and Continue are revolutionizing how we code by offering intelligent code suggestions, complete function generation, and other advanced features that streamline the development process, improve code quality, and significantly boost developer productivity. In this hands-on workshop, Jeffery Payne will guide you through leveraging these powerful tools to accelerate your coding, reduce errors, and unlock new levels of creativity. Learn how to...

Tariq King
Test IO
TE

A Quality Engineering Introduction to AI and Machine Learning

Tuesday, June 10, 2025 - 1:00pm to 4:30pm

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...