Published on December 11, 2023, 10:12 pm

Customizing Foundation Models: Incorporating Generative Ai Into Small Enterprises And Individual Developer Applications

Generative AI, or generative artificial intelligence, holds great potential for enhancing human productivity. However, developing and training the foundation models necessary for its deployment can be a challenging task that requires skills and resources. This article explores how small enterprises and individual developers can incorporate generative AI into their applications by creating and deploying custom versions of larger foundation models.

There are two main challenges in developing generative AI models. First, collecting the data required to train these models is a complex process that has become even more challenging due to intellectual property rights concerns. Second, the resources needed for training can be expensive. Despite these challenges, the societal value of generative AI technologies remains high.

To overcome these challenges, many enterprises and developers start with large generative AI models as a foundation and customize them to fit their specific needs. Developing new generative AI models requires significant investment and effort, but starting with existing models saves time and resources. By focusing on the specific use case and narrowing the scope of the project, developers can adapt these foundation models to meet their requirements at a fraction of the development effort.

When defining a use case for generative AI, several factors should be considered. These include data requirements, such as the type and amount of training data needed; model requirements, including size, performance, openness, and explainability; application requirements like accuracy, latency, privacy, and safety standards; and compute requirements to meet these needs.

Intel provides heterogeneous AI hardware options that cater to various compute requirements. They offer optimized versions of data analysis tools as well as end-to-end AI tools for better hardware utilization. Additionally, Intel provides optimized models that work efficiently on their hardware. Developers can leverage pre-trained models on platforms like Hugging Face or GitHub or use Intel-optimized tools for tasks like data preprocessing and fine-tuning.

Several use cases can illustrate how generative AI can enhance different applications. In customer service, chatbots based on large language models can provide instant answers to common questions, freeing up customer service representatives to handle more complex cases. Retailers can leverage generative AI to offer customers a better online experience by allowing them to virtually try on clothes before purchasing. In healthcare, generative AI can be used to generate personalized reports or recommendations based on real-time patient monitoring data.

Each use case requires different methods of customization, such as fine-tuning the model or using retrieval-based methods. Fine-tuning updates the foundation model with custom information, while retrieval-based methods fetch information from an external database. The choice depends on latency requirements and the need for traceability. Additionally, compute requirements must be considered, especially when deploying on edge devices.

When embarking on a generative AI project, it is essential to define the use case and assess the available resources and frameworks in the AI ecosystem. Implementing fine-tuning and model optimization techniques specific to the use case is crucial. Intel offers access to various CPUs, GPUs, and AI accelerators through their Developer Cloud platform for developers to experiment with different compute options.

In conclusion, incorporating generative AI into applications is feasible for small enterprises and individual developers by leveraging existing foundation models and customizing them according to specific use cases. By considering factors like data, model, application, and compute requirements, developers can maximize project flexibility and optimize resources for deployment. Intel’s hardware options and optimized software tools further assist in developing efficient generative AI solutions across various domains.

To learn more about Intel’s offerings in AI software development and explore their developer resources, visit their website or check out Intel Developer Cloud for access to diverse hardware options.

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