DELL D-GAI-F-01 Practice Test - Questions Answers, Page 4

The training phase of the generative AI lifecycle typically requires the largest amount of resources. This is because training involves processing large datasets to create models that can generate new data or predictions. It requires significant computational power and time, especially for complex models such as deep learning neural networks. The resources needed include data storage, processing power (often using GPUs or specialized hardware), and the time required for the model to learn from the data.

In contrast, deployment involves implementing the model into a production environment, which, while important, often does not require as much resource intensity as the training phase. Inferencing is the process where the trained model makes predictions, which does require resources but not to the extent of the training phase. Fine-tuning is a process of adjusting a pre-trained model to a specific task, which also uses fewer resources compared to the initial training phase.

The Official Dell GenAI Foundations Achievement document outlines the importance of understanding the concepts of artificial intelligence, machine learning, and deep learning, as well as the scope and need of AI in business today, which includes knowledge of the generative AI lifecycle1.

Pretrained Large Language Models (LLMs) like GPT-3 are advantageous for a company with limited resources because they have already been trained on vast amounts of data. This pretraining process involves significant computational resources over an extended period, which is often beyond the capacity of smaller companies or those with limited resources.

Advantages of using pretrained LLMs:

Cost-Effective: Developing a language model from scratch requires substantial financial investment in computing power and data storage. Pretrained models, being readily available, eliminate these initial costs.

Time-Saving: Training a language model can take weeks or even months. Using a pretrained model allows companies to bypass this lengthy process.

Less Data Required: Pretrained models have been trained on diverse datasets, so they require less additional data to fine-tune for specific tasks.

Immediate Deployment: Pretrained models can be deployed quickly for production, allowing companies to focus on application-specific improvements.

In summary, the main advantage is that pretrained LLMs save time and resources for companies, especially those with limited resources, by providing a foundation that has already learned a wide range of language patterns and knowledge. This allows for quicker deployment and cost savings, as the need for extensive data collection and computational training is significantly reduced.