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Ivy: The One-Stop Interface for AI Model Deployment and Development

Daniel Lenton on how Ivy connects frameworks and compiler infrastructures for optimal AI solutions.


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Daniel Lenton is the CEO of Ivy1, a suite of tools designed to accelerate AI Model Development and Model Deployment. Ivy serves as a glue that connects various frameworks and compiler infrastructures, making them compatible. We discuss the benefits in AI model development and deployment, and Daniel highlights Ivy’s role as a connector of different frameworks and compiler infrastructures, its cost-effectiveness, and its ability to improve the longevity of code.

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Daniel Lenton will be at the AI Conference in San Francisco (Sep 26-27). Use the discount code FriendsofBen18 to save 18% on your registration.



Interview highlights – key sections from the video version:

  1. Deployment related challenges facing AI teams
  2. How are teams currently addresssing these deployment challenges?
  3. What are the benefits of using Ivy to deploy AI (and Generative AI) models?
  4. Workflow for using Ivy for deployment
  5. Knobs and parameters Ivy makes available to AI teams needing to optimize models for deployment.
  6. Ivy and fine-tuning foundation models
  7. Early observations from teams who are using Ivy for deployment.
  8. Challenges facing team who are building and developing AI models.
  9. How does Ivy solve the fragmentation and lack of standardization in AI Model development?
  10. Status of Ivy’s transpiler
  11. Ivy’s model development tools – feedback from early users.
  12. Benchmarking and accelerating a group of models on Hugging Face
  13. Contributing and following the Ivy open source projects.

 

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[1] Ben Lorica is an advisor to Ivy and other startups.

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