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.
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.
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.
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.
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.
Machine learning has been traditionally focused on the important initial training phase. This ensures that a model will provide the best outcomes for the expected live data stream - at least for a while. However, it is well known within the machine learning community that however good a job you do at training a model, your results will
The next generation of enterprise applications will increasingly be AI/ML models applied to accelerate existing processes or solve new problems such as accelerating drug discovery and development in life sciences. Kubeflow is an open source reference architecture for AI/ML platform initiated by Google and contributed by several IT platform infrastructure leaders in the industry such as IBM, Redhat, Cisco, Dell, AWS for on-prem and hybrid deployment of AI/ML.