Model Optionality: The Critical Need for AI Project Portability

Editor’s note: Ivan Lee is a speaker for the month-long AI Builders Summit starting on January 15th! Be sure to check out his talk on January 16th, “Cracking the Code: How to Choose the Right LLMs Model for Your Project”!

The AI landscape is rapidly evolving, with new and more powerful models constantly emerging. This has led to a shift away from reliance on a single model towards model specialization. While this trend offers benefits, it also increases the risk of vendor lock-in. To mitigate this and ensure that AI projects can adapt to advancements, organizations must prioritize AI project portability.

Benefits of AI Project Portability

• Adaptability: The ability to easily adapt to new models as they are released.
• Reduced Vendor Lock-In: Avoiding reliance on a single vendor minimizes costs and maintains flexibility.
• Optimized AI Efficiency: A portable AI infrastructure allows for seamless model swapping, enabling the adoption of the best solutions as they emerge.

Building a Portable AI Infrastructure

• Model Agnostic Design: Develop AI applications that are not tied to a specific model.
• Standardized Data Formats: Utilize standardized data formats to ensure compatibility across various models and platforms.
• Flexible Training Pipelines: Create training pipelines that can be easily adapted to different models.

The Future of AI: A Model-Agnostic Approach

A model-agnostic approach will be crucial for organizations to future-proof their AI initiatives. This presentation will walk through shifting our mindset towards building with a flexible, model-agnostic approach. This will allow us to take advantage of the latest model developments and support a robust, multi-model architecture.

Come learn more at the AI Builders Summit, where I’m giving the talk “Cracking the Code: How to Choose the Right LLMs Model for Your Project.” I’ll run through evaluating and comparing multiple models, and deploy our chosen model to a usable endpoint. We will also discuss scoring the results of each model and using those scores to further iterate upon and finetune the models. Finally, we’ll approach all of this in a way that you can swap out the underlying foundation model at any point.

About the Author/Speaker

Ivan Lee graduated with a Computer Science B.S. from Stanford University, then dropped out of his master’s degree to found his first mobile gaming company Loki Studios. After raising institutional funding and building a profitable game, Loki was acquired by Yahoo. Lee spent the next 10 years building AI products at Yahoo and Apple and discovered there was a gap in serving the rapid evolution of Natural Language Processing (NLP) technologies. He built Datasaur to focus on democratizing access to NLP and LLMs. Datasaur has raised $8m in venture funding from top-tier investors such as Initialized Capital, Greg Brockman (President, OpenAI) and Calvin French-Owen (CTO, Segment) and serves companies such as Google, Netflix, Qualtrics, Spotify, the FBI and more.