When it comes to generative AI models, bigger is not necessarily better. More importantly, use tools specifically designed for the task you're trying to accomplish. Large language models (LLMs) trained on huge datasets may seem appealing to organizations looking to leverage as much data as possible, but they may not provide the most accurate results There is a possibility. But smaller, customized models trained on more targeted datasets give companies the ability to take control of their data and ensure quality. This is important as you scale your generative AI use cases to gain a competitive edge.
Vice President of EMEA Field Engineering at Databricks.
Customization is the key
Last year put LLM in the spotlight as the engine behind generative AI applications. While these are fine for consumers, there are many reasons why off-the-shelf general-purpose LLMs are not necessarily ideal for businesses. For example, organizations are not always able to inject their own data into these LLMs, so the model's responses are not always relevant to the internal context. Additionally, many large-scale LLMs require users to grant access to data collection by model authors, which can raise data privacy concerns.
Using smaller, more customized language models can alleviate many of these concerns. Organizations can use their own data to train smaller, customized models. This means that responses take into account internal context and are more relevant. An additional advantage of this approach is that organizations do not have to share data with third parties, ensuring compliance with regulatory requirements while keeping data safe. Smaller customized models can also alleviate efficiency issues, as training a generalized LLM requires significant time, money, and computing power. For example, the larger the model, the more graphics processing units (GPUs) it requires for its functionality. However, GPUs are in short supply and expensive to obtain as demand outstrips supply. Of course, businesses want to keep costs low, so the fewer GPUs needed to train models and perform inference, the better.
Working together to drive more innovation
Collaboration across organizations is also essential, as the majority of senior leaders aim to efficiently scale generative AI projects and consider the benefits of customized models. The generative AI boom is similar to other industrial revolutions. Teams across the organization can benefit from working together to innovate and move forward. This approach also provides research and development benefits.
Compared to general-purpose LLMs, these custom-built models allow developers to create more specialized systems with unique use cases in mind. These allow organizations to focus on generative AI projects that are not only scalable but also likely to generate a higher return on investment because the models are built to address a company's specific problems and opportunities. can do.
Basics are important
When building small, customized models, organizations need to use an open, unified foundation for all data and governance. One way to do this is to use a data intelligence platform that helps ensure the quality, accuracy, and accessibility of the data behind language models. It's also important to explore ways to democratize this data, making it easier for employees across the enterprise to query corporate data using only natural language. This allows non-technical staff to quickly gain the same insights as seasoned data scientists and analysts, freeing up in-house experts to focus on more advanced innovative tasks and even business You can focus on critical tasks. For companies that have traditionally relied on third parties for data analysis, this approach saves time, reduces costs, and puts innovation in the hands of their own employees. Therefore, providing access to data intelligence and using smaller-scale models fosters innovation and facilitates data-driven decision-making at scale.
Close collaboration across the industry is essential to fostering the global growth of generative AI. The more we collectively understand technology, the more productively we can use it. Similarly, as senior leaders continue to focus on efficiency, they must consider the benefits of a right-sized and customized open source model. These models are effective tools for business growth, helping organizations reduce costs, manage data, and ensure quality as they continue to expand their generative AI use cases. By investing in an efficient generative AI strategy, your organization can future-proof and streamline your processes for years to come.
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