With the rapid adoption of generative AI, more and more companies are infusing AI models and services into their products. However, many of these companies are likely to lose business and valuable revenue due to their lack of investment in MLOps. Typically, organizations developing AI systems have relied on training metrics like accuracy, precision and recall, but software quality goes beyond that. Now that the barrier to entry for AI tools is smaller, we need to set quality standards, test practices, and think about AI ethics and safety. Ensuring the quality of AI goes beyond traditional...
Carlos Kidman
Qualiti
Carlos Kidman is a Director of Engineering at Qualiti but was formerly an Engineering Manager at Adobe. He is also an instructor at Test Automation University with courses around architecture, design, containerization, and Machine Learning. He is the founder of QA at the Point, which is the Testing and Quality Community in Utah, and does consulting, workshops, and speaking events all over the world. He streams programming and other tech topics on Twitch, has a YouTube channel, builds open-source software like Pylenium and PyClinic, and is an ML/AI practitioner. He loves fútbol, anime, gaming, and spending time with his wife and kids.