Verta, Inc. Blog Adding GenAI to a Traditional ML Stack

This is part two of a series, where we'll guide you through the essential elements your company needs for a successful GenAI initiative. Check our part 1 here. Many companies and teams have experienced success with traditional deterministic ML, whether through regressions, classifiers, object detection, or various other ML applications. Not all problems are well-suited for generative AI, but if there are programs or initiatives that you previously struggled with, GenAI might provide solutions. But building with generative AI introduces key differences. Here's what to keep in mind as you take the plunge:Generative models are larger and more expensive to train, making the use of third-party or open-source models much more likely. Generative AI training (and even fine tuning) in-house does not make sense for the vast majority of organizations, but readily available APIs allow you to leverage third-party models. Open-source models present a great opportunity at a lower price point and on your familiar tech stack.