Model Ownership

Artificial intelligence (AI) is already turning out to be the transformative technology of the next decade. Organizations that adopt AI judiciously, will benefit from all that this technology offers. They will streamline processes, dramatically improve employee productivity, reduce time to market, harden supply chains, and much more. Those that either do not adopt AI or fail to implement it in the right way will not reap these benefits and risk significant lost opportunity and loss of competitive position.

Any large organization launching a strategic AI initiative needs to choose a platform that is capable of meeting the breadth of use cases that are found in every enterprise. For a model to be able to support those use cases, it needs to be very large, likely over a trillion parameters. Individual small models are not accurate across different domains and attempts to use multiple smaller models will only result in a management nightmare, driving up costs and complexity. But it is not enough to simply use a large model, it is imperative that the organization owns that model.

Large models are pre-trained on open datasets. They have huge amounts of general knowledge that enables them to answer questions on a broad range of topics and perform actions such as document summation or text generation. Having been trained on these large, open datasets they have a lot of general knowledge, but no knowledge specific to a given organization. To make a model effective in enterprise, it must be fine tuned on the organization's private, internal data.

Fine tuning a model on private internal data is the only practical way to ensure that the model correctly understands the nuances found in a given organization. This allows the model to understand internal part numbers, customer information, marketing programs, supply chains, and every other aspect of the business.

It also means putting that data within the model itself and this is why it is so important that the enterprise actually owns their own model. Many of the large models available today are cloud-based models that are offered as a service. These models are owned by the company that created them.

To fine-tune these models a company must send their data to the model, which typically resides within the vendor's cloud. Once the data is within the model, it becomes a part of the model. This means that the company’s private internal data, the most valuable data that it possesses, is now under the control and ownership of another company.  

Model vendors may promise to keep the data secure, but for large organizations, publicly traded companies, those in regulated industries, and government agencies, that will not be sufficient. At a minimum, moving data outside the control of the enterprise will certainly violate any data governance policies. In some cases, it may not even be legal.

If a company does utilize a cloud-based model provider, as they continue to fine tune the model and get better results what they are doing is locking themselves into that vendor. Over time, the model will get better and better but if the company chooses to use a different model at some point, for example if a more effective platform to run models becomes available, they will be unable to change. They do not own the model and they cannot use it on any other platform. If they wish to use another model, they will have to completely start over.

With SambaNova, customers always own the models that have been fine tuned on their data. SambaNova Suite can be deployed on-premises within the customer data center, including air gapped environments, or within dedicated private cloud environments so data does not have to leave the customers control. SambaNova Suite was built to ensure that the customer is always in control of all their data.

SambaNova Suite includes the Samba-1 generative AI model, which is the first trillion parameter model with the accuracy, trainability, capacity to address every use case, data privacy, and security to meet the needs of the enterprise. Using a Composition of Experts (CoE) architecture which aggregates multiple smaller models into a single large model, Samba-1 delivers the benefits of both large and small models, without any of the drawbacks of either.

As enterprises incorporate generative AI into their business processes, ensuring that they retain ownership of the models they use is one of the most important steps that they will take.