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Full Elasticity as the Future of AI

There is no question that elasticity — both in the sense of on-demand compute resource management and flexibility for anyone to work with data on top of those resources — is the future of Enterprise AI.

Compute now and in the futureAI transformation must be prevalent across the organization, not just focused on specific teams or people. That necessarily means the ability to elastically scale to match data location and cost constraints.

While cloud enables leveraging elastic resources for AI computing, the real challenge is to enable everyone (whatever their technological skills) to leverage elastic resources.

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Elasticity to Reduce Dependency

In traditional IT contexts, risk and cost of lock-in are critical in assessment and adoption of new technologies. In the age of Enterprise AI, this phenomenon is magnified exponentially as AI technologies come and go with no sign of reduction in the pace of innovation. In addition, the shift to cloud providers continues to create interdependencies between different components of the full AI stack.

This necessarily means that companies have to become more elastic, able to adopt and drop technologies as they arise (considering, of course, when those technologies make sense for the business, not just following every trend that crops up). It also means that in order to provide an experience for end users who are interacting with data and AI systems that is unchanging despite underlying architecture, organizations need to have a layer that sits on top of existing systems.


Dataiku Brings Automated Elasticity

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Its unique data and computation abstraction approach supports global organizations toward their move to full elasticity for AI. With Dataiku:

  • Push computation to Cloud services and DWH (e.g., Snowflake, BigQuery, etc.).
  • Provide a unified way to run in-memory, machine learning, and Spark jobs on Kubernetes.
  • Automatically scale computing resources up or down according to usage. 

Dataiku continuously integrates the most recent technologies in its stack in order to lower the barrier to integration for the companies themselves. These include computation, storage, programming languages, machine learning technologies and more.


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