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Remember a year ago, all the way back to last November <\/a>before we knew about ChatGPT, when machine learning was all about building models to solve for a single task like loan approvals or fraud protection? That approach seemed to go out the window with the rise of generalized LLMs, but the fact is generalized models aren\u2019t well suited to every problem, and task-based models are still alive and well in the enterprise.<\/p>\n These task-based models have, up until the rise of LLMs, been the basis for most AI in the enterprise, and they aren\u2019t going away. It\u2019s what Amazon CTO Werner Vogels referred to as \u201cgood old-fashioned AI\u201d in his keynote this week, and in his view, is the kind of AI that is still solving a lot of real-world problems.<\/p>\n Atul Deo, general manager of Amazon Bedrock, the product introduced earlier this year<\/a> as a way to plug into a variety of large language models via APIs, also believes that task models aren\u2019t going to simply disappear. Instead, they have become another AI tool in the arsenal.<\/p>\n \u201cBefore the advent of large language models, we were mostly in a task-specific world. And the idea there was you would train a model from scratch for a particular task,\u201d Deo told TechCrunch. He says the main difference between the task model and the LLM is that one is trained for that specific task, while the other can handle things outside the boundaries of the model.<\/p>\n Jon Turow, a partner at investment firm Madrona, who formerly spent almost a decade at AWS, says the industry has been talking about emerging capabilities in large language models like reasoning and out-of-domain robustness. \u201cThese allow you to be able to stretch beyond a narrow definition of what the model was initially expected to do,\u201d he said. But, he added, it\u2019s still very much up for debate how far these capabilities can go.<\/p>\n Like Deo, Turow says task models aren\u2019t simply going to suddenly go away. \u201cThere is clearly still a role for task-specific models because they can be smaller, they can be faster, they can be cheaper and they can in some cases even be more performant because they\u2019re designed for a specific task,\u201d he said.<\/p>\n But the lure of an all-purpose model is hard to ignore. \u201cWhen you\u2019re looking at an aggregate level in a company, when there are hundreds of machine learning models being trained separately, that doesn\u2019t make any sense,\u201d Deo said. \u201cWhereas if you went with a more capable large language model, you get the reusability benefit right away, while allowing you to use a single model to tackle a bunch of different use cases.\u201d<\/p>\n For Amazon, SageMaker, the company\u2019s machine learning operations platform, remains a key product, one that is aimed at data scientists instead of developers, as Bedrock is.\u00a0It reports<\/a> tens of thousands of customers building millions of models. It would be foolhardy to give that up, and frankly just because LLMs are the flavor of the moment doesn\u2019t mean that the technology that came before won\u2019t remain relevant for some time to come.<\/p>\n Enterprise software in particular doesn\u2019t work that way. Nobody is simply tossing their significant investment because a new thing came along, even one as powerful as the current crop of large language models. It\u2019s worth noting that Amazon did announce upgrades to SageMaker<\/a> this week, aimed squarely at managing large language models.<\/p>\n Prior to these more capable large language models, the task model was really the only option, and that\u2019s how companies approached it, by building a team of data scientists to help develop these models. What is the role of the data scientist in the age of large language models where tools are being aimed at developers? Turow thinks they still have a key job to do, even in companies concentrating on LLMs.<\/p>\n \u201cThey\u2019re going to think critically about data, and that is actually a role that is growing, not shrinking,\u201d he said. Regardless of the model, Turow believes data scientists will help people understand the relationship between AI and data inside large companies.<\/p>\n \u201cI think every one of us needs to really think critically about what AI is and is not capable of and what data does and does not mean,\u201d he said. And that\u2019s true regardless of whether you\u2019re building a more generalized large language model or a task model.<\/p>\n That\u2019s why these two approaches will continue to work concurrently for some time to come because sometimes bigger is better, and sometimes it\u2019s not.<\/p>\n