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Case studies from the GenAI frontier, scaling ML teams, from biologist to machine learning consultant- Erik Gafni - The Data Scientist Show #082
Erik Gafni builds AI systems and teams. He founded Eventum AI (https://bit.ly/eventum-ai), an ML consulting company working with high-growth startups. We talked about GenAI projects he worked on, how he built production ML systems, how to scale ML teams, and his journey from biologist to ML researcher.
- Interested in working with Erik: https://bit.ly/erik-consulting
- Erik's LinkedIn: https://bit.ly/erik-gafni-LI
(00:00:00) Introduction
(00:01:59) Is GenAI overhyped?
(00:04:28) Ascent translation with AI
(00:11:58) Social media app with AI
(00:14:00) Stable diffusion model evaluation
(00:15:57) "Consult-to-hire" model
(00:17:35) AI in biotech
(00:22:46) Self-supervised learning
(00:31:22) How he hires people
(00:33:19) Research vs production
(00:35:57) Is AGI coming?
(00:37:30) New trends in GenAI
(00:41:45) Data quality in GenAI
(00:42:58) Philosophy in LLMs
(00:49:48) OpenAI vs Open Source
(00:53:58) Mistakes he made
(00:57:41) How did he get into ML
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By Daliana Liu
4.7
7575 ratings
Erik Gafni builds AI systems and teams. He founded Eventum AI (https://bit.ly/eventum-ai), an ML consulting company working with high-growth startups. We talked about GenAI projects he worked on, how he built production ML systems, how to scale ML teams, and his journey from biologist to ML researcher.
- Interested in working with Erik: https://bit.ly/erik-consulting
- Erik's LinkedIn: https://bit.ly/erik-gafni-LI
(00:00:00) Introduction
(00:01:59) Is GenAI overhyped?
(00:04:28) Ascent translation with AI
(00:11:58) Social media app with AI
(00:14:00) Stable diffusion model evaluation
(00:15:57) "Consult-to-hire" model
(00:17:35) AI in biotech
(00:22:46) Self-supervised learning
(00:31:22) How he hires people
(00:33:19) Research vs production
(00:35:57) Is AGI coming?
(00:37:30) New trends in GenAI
(00:41:45) Data quality in GenAI
(00:42:58) Philosophy in LLMs
(00:49:48) OpenAI vs Open Source
(00:53:58) Mistakes he made
(00:57:41) How did he get into ML
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