Data is the fuel for artificial intelligence and poses new challenges for organizations looking to extract value with generative AI (GenAI). Bringing both together to deliver relevant and valuable outcomes that move their business forward is not as straightforward, reflecting the challenges customers face.
In this episode, listen to a lighthearted and honest conversation about generative AI’s role with an organization’s data—being akin to being a new employee, starting in a new job without all the knowledge of experienced employees.
Like employees gaining on-the-job training and finding their way in their environment, generative AI models can also be trained with organizational data, enabling data scientists to start making them more specific to their organization.
Rather than training models from scratch, organizations can get a head start with pretrained models and fine tune them with their own data, such as from their content, their communication channels like Slack, data from Salesforce or workflow operations such as ServiceNow. A customized trained model and approach is now like a trained employee contributing significant value to the business. Manuvir further elaborates that trained models are put to work, embedded into applications such as chatbots and ready for queries at scale.
Deploying a generative AI strategy relies on a combination of hardware, software and experience, which enables the business to realize value from their data. Customers want the flexibility to choose the infrastructure and approaches that best enable them with a head start. Dell Validated Designs for Generative AI with NVIDIA innovation deliver the full-stack solution blueprint from which customers can extract value with generative AI in their organization.
Learn more here.
