<aside> 💡 This is the text companion to the Moonfire Academy session by Mike Arpaia on Friday, 24 March 2023 @ 2pm GMT
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As Transformer-based language models (LLMs, GenAI, etc.) gain popularity, integrating them into modern applications presents unique engineering challenges. This document aims to address common pitfalls and establish mental models for experienced engineering leaders to effectively leverage LLMs in a sustainable way that minimizes technical debt.
GPT-4 represents a significant step change in foundation model capabilities, offering remarkable advancements in natural language processing, understanding, and generation. For our specific use-case of leveraging machine learning to automate and accelerate the venture capital process, GPT-4 has demonstrated its ability to meet or even exceed the performance of custom models we have spent months developing.
Consider the following precision/recall curve for our venture scale classifier (is this company venture scale or not?):
Given these breakthroughs, our internal machine learning modeling efforts for Q2 will focus on diligently replacing every component of our existing machine learning stack with GPT-4 implementations. This will establish a new baseline and future custom modeling effort will focus on further refinement of those solutions (via fine-tuning, internal models, etc).
As we move forward, it is crucial to remain mindful of the potential for further advancements in foundation models like GPT-4.
Ultimately, the release of GPT-4 has fundamentally altered the landscape of machine learning engineering. For the overwhelming majority of use-cases, the focus has shifted from building custom models tailored to specific tasks to leveraging and fine-tuning powerful pre-trained models like GPT-4. This shift has facilitated rapid prototyping and deployment of solutions across a wide range of applications, significantly reducing the time and resources required for model development. As a result, machine learning engineering has become more accessible and versatile, enabling us to explore new opportunities and applications with greater ease.
In this new paradigm, the role of machine learning engineers is evolving to emphasize skills such as fine-tuning, transfer learning, and understanding how to effectively harness the potential of foundation models like GPT-4 to address novel problems and drive innovation.
Integrating external APIs like OpenAI and Cohere, or utilizing tools like HuggingFace, can offer several benefits, including easy access to state-of-the-art models, reduced development time, and a lower barrier to entry. However, it is essential to consider the trade-offs when deciding whether to use these services or run your own models.
GPT-4 has some specific considerations: