9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems.Join the discussionChangelog++ members save 3 minutes on this episode because they made the ads disappear. Join today!Sponsors:PSSC Labs – Solutions from PSSC Labs provide a cost effective, highly secure, and performance guarantee that organizations need to reach their AI and Machine Learning Goals. Learn more and and get a FREE consultation today at pssclabs.com/practicalaiSnowplow Analytics – The behavioral data management platform powering your data journey. Capture and process high-quality behavioral data from all your platforms and products and deliver that data to your cloud destination of choice. Get started and experience Snowplow data for yourself at snowplowanalytics.comChangelog++ – You love our content and you want to take it to the next level by showing your support. We’ll take you closer to the metal with no ads, extended episodes, outtakes, bonus content, a deep discount in our merch store (soon), and more to come. Let’s do this! Featuring:Roey Mechrez – LinkedIn, XChris Benson – Website, GitHub, LinkedIn, XDaniel Whitenack – Website, GitHub, XShow Notes:NVIDIA GTC21 Talk on “Addressing Garbage In Garbage Out”BeyondMindsSomething missing or broken? PRs welcome!

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