Jordan Fisher is the CEO and co-founder of Standard AI, an autonomous checkout company that’s pushing the boundaries of computer vision.
In this episode, Jordan discusses “the Wild West” of the MLOps stack and tells Lukas why Rust beats Python. He also explains why AutoML shouldn't be overlooked and uses a bag of chips to help explain the Manifold Hypothesis.
Show notes (transcript and links): http://wandb.me/gd-jordan-fisher
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⏳ Timestamps:
00:00 Intro
00:40 The origins of Standard AI
08:30 Getting Standard into stores
18:00 Supervised learning, the advent of synthetic data, and the manifold hypothesis
24:23 What's important in a MLOps stack
27:32 The merits of AutoML
30:00 Deep learning frameworks
33:02 Python versus Rust
39:32 Raw camera data versus video
42:47 The future of autonomous checkout
48:02 Sharing the StandardSim data set
52:30 Picking the right tools
54:30 Overcoming dynamic data set challenges
57:35 Outro
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Connect with Jordan and Standard AI
📍 Jordan on LinkedIn: https://www.linkedin.com/in/jordan-fisher-81145025/
📍 Standard AI on Twitter: https://twitter.com/StandardAi
📍 Careers at Standard AI: https://careers.standard.ai/
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💬 Host: Lukas Biewald
📹 Producers: Riley Fields, Cayla Sharp, Angelica Pan, Lavanya Shukla
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