AI Superstream: Building with Open Source Generative AI Models and Frameworks
by Susan Shu Chang, James Spiteri, Denys Linkov, Avin Regmi, Mandy Gu, Nicole Königstein, Leandro von Werra
Overview
The landscape of open source technologies for building AI applications has expanded rapidly since the advent of LLMs and other frontier AI models. But there are ongoing discussions about what constitutes open source AI, and current technologies come with a range of limitations and benefits. Proprietary models may be optimal in some cases, but concerns about cost, privacy, and security for production-grade applications may require a closer look at open source options.
Join us to explore the features and capabilities of the latest open source AI models, frameworks, and platforms, from Mistral and LangChain to Hugging Face. You'll learn how to leverage these and other open source AI tools to build more robust and cost-effective AI applications, right from the industry experts already putting them to work in the field.
What you’ll learn and how you can apply it
- Understand the benefits of open source AI tools versus proprietary tools and models
- Learn the potential limitations of open source AI technologies and how to navigate them
This live course is for you because…
- You’re an AI practitioner who’s building applications with the latest generative AI technologies.
- You’re leading teams who are building generative AI applications.
- You’re curious about the latest debates and current state of open source AI technologies.
Recommended follow-up:
- Read Use Open Source for Safer AI Experiments (article)
- Read Prompt Engineering for Generative AI (early release book)
- Read Prompt Engineering for LLMs (early release book)
- Watch What’s New in AI: Open Source Large Language Models with Eric Xing (video)
- Watch AI Catalyst Conference: Building Commercially Successful LLM Applications (on-demand course)
Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event.
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