Learn how to confidently launch AI products by testing models against human judgment, monitoring performance drift, and using dashboards to track key metrics,ensuring your AI stays...
Preview Course
Launching an AI product isn’t the finish line—it’s the starting point of continuous learning and improvement. In this module, you’ll discover how to run A/B tests for AI systems, comparing model predictions with human judgment to know when to trust automation and when to step in. You’ll also explore real-world cases where structured testing ensures your AI delivers value, while minimizing risks.
Next, we’ll dive into the critical practice of monitoring model drift, why platforms like TikTok constantly retrain their recommendation systems to stay relevant and how product managers can spot these shifts early. Finally, you’ll learn how to leverage AI performance dashboards to track key metrics, surface potential problems, and guide data-driven iteration. By the end of this module, you’ll be equipped with the tools and mindset to keep your AI products resilient and effective long after launch.
Course curriculum Empty
Basic computer literacy
No coding or math knowledge required.
Curiosity to learn how AI models behave after launch and how PMs monitor them
Detect and respond to model drift to keep AI systems relevant and reliable.
Design and run A/B tests to evaluate AI models against human judgment.
Use an AI performance dashboard to track metrics like accuracy, precision, and engagement.
Confidently guide post-launch iteration to ensure AI products deliver long-term impact.
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