Towards stability and robustness
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.…
We score an episode from what we can actually measure — its reach and engagement, what listeners say about it, and what it covers. We don't have those signals for this one yet, so it doesn't get a number.
Buzzmeter says it's buzzing across platforms — the Hive Vote says whether the people who actually listened liked it.
Rate this episode
Add your vote to the Hive — listeners rate every episode after they finish.
- No reviews yet — be the first.
Similar episodes from other shows
More like Practical AI →
[Practical AI] AI Trends: a Latent Space x Practical AI crossover pod!
Stop being skeptical about AI for development with Charity Majors
10 OpenClaw Lessons for Building Agent Teams
Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google, and Amazon
One great episode in your inbox, daily — free.
One email a day, unsubscribe anytime. No spam, ever.