Raviteja Yelamanchili shares how Scale AI transformed banking cycles from one year to real-time and why your most valuable enterprise data isn't being collected.

Topics Include:

• Scale evolved from data annotations company to enterprise AI solutions provider
• Healthcare system transformed patient transcriptions into value using reinforcement learning research
• Blank slate customer problems allow Scale to experiment with latest methods
• Many customers propose solutions before explaining their actual underlying business problems
• Biggest AI misconception: technology will replace jobs rather than augment productivity
• Don't wait for perfect AI—start learning through iteration and evolution now
• Banking credit cycle transformed from one-year process to real-time strategic insights
• Scale deploys flexibly across EC2, EKS, or Bedrock based on customer requirements
• Enterprises want business value generation more than academic research papers alone
• Next 12-24 months focus: making data consumable and leveraging unused datasets
• Tribal knowledge from experienced SMEs represents most valuable yet uncollected data
• Agent-based learning captures expertise through feedback loops on Scale's SGP platform

Participants:

• Raviteja Yelamanchili - Head of Solution Engineering, Scale AI

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