More companies are building ML and AI systems and applications, but they lack the same rigorous testing because many Testers don't know how to approach testing them. After having built and tested many different ML models and systems and talking to ML teams of small and large organizations, one thing always stands out: "We need better testing and automation in our MLOps lifecycle." So, we'll start by demystifying Machine Learning by breaking down a Prediction application so the audience better understands the "magic algorithms" behind the scenes. We'll explore an example of ML systems that...
Carlos Kidman
Director of Engineering
Stealth Startup
Carlos Kidman is a Director of Engineering at an AI company, 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.