Towards stability and robustness (Practical AI #141)

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.

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.

Leave us a comment

Changelog++ 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/practicalai
Snowplow 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.com
Changelog++ – 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 – Twitter, LinkedInChris Benson – Twitter, GitHub, LinkedIn, WebsiteDaniel Whitenack – Twitter, GitHub, Website

Show Notes:

NVIDIA GTC21 Talk on “Addressing Garbage In Garbage Out”
BeyondMinds

Something missing or broken? PRs welcome!