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We Can All Be AI Engineers and We Can Do It with Open Source Models // Luke Marsden // #273
Nov 20, 2024
Luke Marsden, CEO of HelixML and a seasoned tech leader, dives into the world of open-source AI models. He discusses how anyone can become an AI engineer, emphasizing the practicality of building Generative AI applications. Luke elaborates on the advantages of open-source solutions for data privacy and business value. He also highlights the importance of structured specifications and customization in AI systems, making advanced features accessible for both technical and non-technical users. Join him for insights into the future of AI innovation!
51:08
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Quick takeaways
- Establishing clear standards and utilizing CI/CD principles is vital for the effective deployment and management of generative AI applications.
- Non-technical users can rapidly prototype AI applications through user-friendly interfaces, fostering collaboration and innovation in AI development across industries.
Deep dives
The Genesis of AI Specs
Creating standards for AI applications is essential for their effective deployment and management. The discussion centers around the integration of testing and deployment practices typically utilized in software engineering, specifically applying CI/CD principles to generative AI applications. This involves the generation of evaluation criteria and specifications in YAML format, allowing for easier version control and consistency. By establishing clear standards, developers can ensure the production readiness of their AI applications, thus enabling better integration with existing software engineering practices.
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