Daniel Lenton on how Ivy connects frameworks and compiler infrastructures for optimal AI solutions.
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.
Interview highlights – key sections from the video version:
- Deployment related challenges facing AI teams
- How are teams currently addresssing these deployment challenges?
- What are the benefits of using Ivy to deploy AI (and Generative AI) models?
- Workflow for using Ivy for deployment
- Knobs and parameters Ivy makes available to AI teams needing to optimize models for deployment.
- Ivy and fine-tuning foundation models
- Early observations from teams who are using Ivy for deployment.
- Challenges facing team who are building and developing AI models.
- How does Ivy solve the fragmentation and lack of standardization in AI Model development?
- Status of Ivy’s transpiler
- Ivy’s model development tools – feedback from early users.
- Benchmarking and accelerating a group of models on Hugging Face
- Contributing and following the Ivy open source projects.
- A video version of this conversation is available on our YouTube channel.
- Navigating the Intricacies of LLM Inference & Serving
- Specialized Hardware for AI: Rethinking Assumptions and Implications for the Future
- Dylan Patel: The Open Source Stack Unleashing a Game-Changing AI Hardware Shift
- Brian Raymond: ETL for LLMs
- Jerry Liu: An Open Source Data Framework for LLMs
- Andrew Feldman: The Rise of Custom Foundation Models
- Tim Davis: Redefining AI Infrastructure
- Hagay Lupesko: Custom Foundation Models
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 Ben Lorica is an advisor to Ivy and other startups.