The Private AI Dilemma

Organizations are concerned about using public GenAI models-as-a-service (OpenAI, Bard, or Anthropic) whereby their private data is used to train these public models.  

That means a competitor of the organization can have access to the same GenAI model trained on their data- a rather scary situation! The flipside is that the organization now wants to keep both their data and their GenAI model fully inside their control, inside their firewalls. 

Then it does not have access to the compute and GPU resources to train, fine tune, and deploy the GenAI models.  They reside in public cloud regions around the globe.

Our Solution

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The DKubeX GenAI Ops Platform seamlessly operates within your organization's infrastructure while enabling GenAI models to run efficiently in cost-effective, GPU-equipped cloud environments. 

It ensures data security by maintaining control through encrypted pipelines and firewall-based storage of model weights. All public cloud transactions are meticulously logged, providing robust auditing and security measures.

What are the concerns associated with using public GenAI models-as-a-service in a business context?

Many organizations are apprehensive,and rightly so, about using public GenAI models-as-a-service as their private data is employed to train these models. This could result in competitors gaining access to the same models, which is a source of concern.

What challenges do organizations face when they want to maintain control over their data?

Common challenges include the lack of access to the necessary compute and GPU resources for training, fine-tuning, and deployment. These models tend to be located in public cloud regions worldwide making data privacy a nightmare.

How does DKubeX address these challenges?

DKubeX serves as a GenAI ops platform that can be deployed within an organization's on-premises environment, private cloud, or VPC cloud. 

It provides the means to seamlessly run GenAI models in any cloud or cloud region with cost-effective GPUs, thanks to its robust integration with Skypilot. 

While data may be transmitted to the public cloud, it remains under the full control of the organization, safeguarded by encrypted channels. Model weights are stored within the organization's firewalls, and all input/output transactions to the public cloud are logged and maintained for auditing and security purposes.

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