Generative AI has the potential to greatly enhance human productivity, but the development and training of foundation models can be challenging for many enterprises and individual developers. The two main challenges are collecting the necessary training data and the cost of resources for training. However, the value of generative AI technologies remains high, and there are ways for small enterprises and individual developers to incorporate it into their applications.
Customizing Foundation Models
One approach is to create and deploy custom versions of larger foundation models. While it requires a significant investment and effort to develop new generative AI models, starting with a large foundation model can save development time and effort. By customizing the model to fit specific use cases, enterprises and developers can find the best fit for their requirements and budget.
This approach also provides flexibility in infrastructure by using existing CPUs or AI accelerators instead of specific GPUs, which may be in short supply. By focusing on the specific use case and narrowing the scope, developers can maximize project flexibility while using open, standards-based software and ubiquitous hardware.
Defining the Use Case
Defining a use case is crucial in software development, as it defines the characteristics of the target user, the problem to be solved, and how the application will be used. Similarly, in generative AI projects, defining a use case can reduce the size, compute requirements, and energy consumption of the AI model. This also leads to reduced development effort and costs.
There are several factors to consider when defining a use case for generative AI:
- Data requirements: What data is necessary and available for training? Is the data structured or unstructured? Are there any regulations or restrictions?
- Model requirements: Consider the size, performance, openness, and explainability of the model.
- Application requirements: Think about the accuracy, latency, privacy, and safety standards that must be met.
- Compute requirements: Determine the compute resources needed to meet the requirements.
Once these factors are understood, enterprises and developers can have productive conversations to define and scope the project requirements.
Optimizing with Intel AI Tools
Intel provides a range of AI hardware options for various compute requirements. They also offer optimized versions of data analysis and AI tools to help teams get the most out of their hardware. Additionally, Intel provides optimized models that are ranked highly in the AI community.
Intel’s AI tools, together with those provided by its developer ecosystem, can satisfy application accuracy, latency, and security considerations. Starting with pre-trained models and using Intel-optimized tools for data processing and fine-tuning can help enterprises and developers customize the models to their specific use cases.
Intel’s optimizations in open source AI frameworks like TensorFlow, PyTorch, and DeepSpeed also contribute to efficient model development.
Generative AI Use Cases
There are several use case examples for generative AI in industries like customer service, retail, and healthcare.
Chatbots based on large language models can improve customer service efficiency by providing instant answers to common questions. Fine-tuning and retrieval methods can customize the foundation model to suit specific business needs. Retailers can use generative AI to offer their customers a more immersive online experience, such as virtual fitting rooms. In healthcare, generative AI can be combined with real-time patient monitoring data to generate personalized reports and interventions.
Considerations for Deployment
When deploying generative AI applications, it’s important to consider the compute requirements and costs for ongoing inference operations. Model optimization techniques like quantization can help reduce latency and inference compute costs.
Choosing the right starting point for the foundation model and customizing it to fit the specific use case is crucial. Intel’s AI tools and frameworks are designed to optimize for deployment across a variety of edge devices without the need for low-level code rewriting.
By defining a clear use case, taking advantage of pre-trained models, using Intel-optimized tools and frameworks, and considering compute requirements, enterprises and individual developers can efficiently incorporate generative AI into their applications.
With the right approach and tools, generative AI can become accessible to small enterprises and individual developers, enabling them to leverage the power of AI for increased productivity and innovation.
