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Extending AI: From Industry to Innovation // Sophia Rowland & David Weik // #247

4 snips
Jul 12, 2024
Sophia and David from SAS discuss challenges in MLOps, integrating generative AI, transitioning to real-time processes, and empowering business users with AI innovation. They also explore obstacles in moving AI models to production, collaboration between data scientists and engineers, and common themes in high-performing ML AI teams.
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INSIGHT

Shift to Real-Time MLOps

  • MLOps is shifting from batch processing to real-time integration requiring data scientists to work directly with application developers.
  • This shift demands new skills for data scientists unfamiliar with APIs, CI/CD, and production environments.
ADVICE

Involve Data Scientists in API Integration

  • Show data scientists how API-based deployment differs from batch and involve them in building simple front-end apps.
  • Reducing required input fields helps smooth integration and reduces production deployment hurdles.
ANECDOTE

Healthcare Capstone Handoff Regret

  • Sophia shared a healthcare capstone project where the team handed off training code instead of a usable model.
  • This flawed handoff left the healthcare organization struggling to use the analytics effectively.
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