The future in a flash

How to make AI work at hyperscale


The-future-in-a-flash

Speak to any company today and they will tell you that future success depends on the data they hold, be it through advanced analytics to build a single view of the customer, or use of machine learning and AI to automate and optimise key business and operational processes. Behind the hype of AI’s potential however, the future is more… let’s say evolutionary.

As many organisations know, data pipelines are restricted by physical hardware constraints. While silicon-based processing and networking continue to advance, many organisations still rely on spinning magnetic disks to store and deliver data. Why? Cost is a factor, but so are the realities of working with an existing environment.

The resulting compromises lead to architectural complexity, engineering and data lifecycle management challenges, across traditional storage, flash and cloud-based models. So, what’s the answer? So we are told, to pick the right model for the job, be it data lakes, stream processing or indeed, AI. In this webinar we cover:

  • Setting the context — comparing the hype around AI with the realities faced by many organisations today
  • Organisational AI evolution — setting out enterprise needs in terms of data management, tools and frameworks
  • The hyperscale potential — opportunities for AI and analytics that come from adopting leading-edge technologies
  • Architectural considerations — when to use what, with what skills, at each stage of an organisation’s journey
  • Value-based considerations — from business value to AI’s greater potential impact on community and society

Supremely conscious of challenge, opportunity and the need to get things right is Ryan Sayre, Global Technologist at flash storage vendor Pure Storage: we pick Ryan’s brains, learning from Pure’s customers about how to define and deploy the right combination of hardware components and software frameworks for the job.

So, if you are struggling with bottlenecks to your analytics and AI delivery, compromising on throughput, or concerned about the skills you need, tune in.

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