A cloud-native OS for AI provides production-ready AI solutions in a fraction of the time.

Why Do We Need an OS for AI?

The need for production AI deployments is gaining more prominence and growing in adoption. New research suggests that the AI market size will grow from $40.74 billion in 2020 to $390.9 billion by 2025. Currently, most companies that have adopted AI, are still only in the development and experimentation phase of their AI journey.

What most of these companies will soon realize is that going from development and experimentation to production is a very difficult and time-consuming process. You have to set up a complex infrastructure that supports a diverse range of tools, workflows, global teams, and regulatory requirements (e.g. GDPR). Investment of human capital and the risk acceptance of potential data breaches in pursuing these types of projects is now a critical component of a calculated growth strategy.

How Is AI Deployed to Production Now?

The hard truth is that most large enterprises are yet to deploy AI in production in a meaningful way. Although this is starting to change and the adoption of AI is now trending, there is more work to be done to raise the engagement of C-level leadership in this space. The early adopters who had the foresight and risk appetite have deployed traditional machine learning workflows using older technologies and approaches. They are now exploring how to migrate or are starting to migrate their old and costly infrastructure - built on a mixture of tools like Spark and Hadoop in favor of more modern, cloud-native solutions.

“The growth of many new open-source tools centered around Kubernetes has spurred the need to string together these tools to form production AI pipelines with glue code.” - Rush Tehrani, CEO Onepanel

So Who Is Actually Deploying AI Projects to Production Today?

Tesla is a great example of a company deploying large scale HPC infrastructure to support its AI pipelines. Having the largest and most complex computer vision pipelines, they have developed and patented their own platform (“OS”).

#open source #kubernates #cloud