Adopting an AI data platform framework lets you define a platform to streamline the processes and approaches to security, data workflows, and how the data platform is managed. Adopting an “AI Factory” approach can streamline your AI strategy, allowing you to focus on key business outcomes.
Any Color You Like…
When the automobile was first invented in the 19th century, they were rare, expensive, and difficult to run reliably – and each required a unique set of skills to drive safely. But in 1908, Henry Ford realised that with a new set of principles, he could create automobiles which were affordable, simple to operate, and durable – by building a standardized platform on which he could innovate, both for rapid development, and ease of manufacture and operation.
Since then, we have seen this same pattern with successive waves of technology, such as radio & television, air travel, personal computers, and the world wide web. First, technology pioneers compete with alternate visions for a new technology. Each vision has its own loyal customer community, but may not be able to cross the chasm alone; but then the real breakthrough occurs as a set of common tools and modular components bring standards for ease-of-use, streamlined manufacturing and simplified support – leading to economies of scale, widespread adoption and repeatability.
Enterprise AI Comes of Age
Fast forward to today, and we are seeing the same happen to AI infrastructure for the Enterprise – AI systems no longer need to be complex and unreliable, Enterprises can transform the way they approach AI systems with a change in perspective, by building a standardized platform across the organization, encouraging innovation, faster development and testing, and ease of operation and rapid ROI.
Even so, Enterprise AI has had its own journey: AI was originally reserved for researchers, and drew expertise from HPC facilities and scientists. As the technology became elevated, hyperscale organisations such as Google, Facebook and Amazon all implemented AI tools to help optimize their business and target customers. And now, as AI in infrastructure became increasingly accessible, first we saw significant advances in Life Sciences, Financial Services systems and Autonomous Automotives, and now we are starting to see Enterprises everywhere look to build AI infrastructure to support their own Digital Transformation.
At the DDN User Group in Fall 2021, our keynote speakers described this journey in terms of the three phases of AI: in the first phase the early adopters included those organizations who wanted to develop initial expertise. In phase two, more and more enterprise companies bring in data scientists and explore using core technologies and start to develop some structure. In the third and final stage of Enterprise AI adoption, enterprises look to build AI infrastructure into wider business applications to support digital transformation – at which point they need to adopt a more mature process and methodology to simplify re-use and collaboration.
Digital Transformation with AI
When an Enterprise undertakes a true Digital Transformation journey, it needs to evaluate parts of its critical workflow – research and development, supply chain, production, logistics, support, sales, and operations. While each of those critical workflows may have different characteristics, Enterprises will adopt a transformation strategy which will help to agree key metrics, tools and processes to move to the new workflow, which will often leverage traditional IT technologies and change management.
Organizations may also see an opportunity to enhance their digital transformation, with AI tools such as image recognition, natural language processing, or predictive maintenance – where new skills and tools may be needed. For emerging technologies like AI, it is essential to build and retain those skills within the organization, as part of their strategic knowledgebase, perhaps within an AI Center of Excellence. And in order to implement and exploit that knowledge in a reliable and repeatable way, an organization can lay down the processes and methodologies that teams should use as part of a standard structure to ensure success.
Adopting an AI factory framework in this way lets you define a platform to streamline the processes and approaches to security, data workflows, and how the data platform is managed. This allows an organisation to focus on the strategic outcomes and long-term goals for their AI program, with a reference architecture that reduces the risk and costs involved in building AI at scale.
Four Key Pillars:
This AI Factory concept is explored by DDN’s James Coomer in Raconteur’s AI for Business special report, published in The Sunday Times, where he looks at how organizations can reduce the risks of AI implementation by moving to an AI Factory model with standardised processes and governance, using the four key pillars of AI strategy:
- Data First – Be clear about the data you need, and how you will gather, prepare and process it. What are the right data sources for training and learning, and how will you capture and organise data for real-time analytics and decision-making?
- Governance – How do you know that the data will be used appropriately, and how can you measure the reliability of the results, and the fairness of the outcomes? And what tools do you need to reassure stakeholders that the data and results are secure?
- People – What tools does your team need to build and implement an AI strategy? Not only the data scientists who need to share knowledge and collaborate, but also for the business leaders to be able to interpret and apply the insights?
- Platform – Ensure you have the right platform to process data efficiently. Many projects fail because of data bottlenecks or because your network and devices cannot process data at scale.
Organisations launching their own AI strategy can learn from the “AI Factory” approach by adopting standardized tools, platforms, and reference architectures which offer a proven platform, sure in the knowledge that they will be able to scale, adapt and diversify – moving the focus from technology and implementation, towards a streamlined production line to deliver faster, more powerful business results.
Get your copy of the Sunday Times, Raconteur’s AI for Business special report to read the full story, and discover how to unlock the promise of AI with James Coomer, SVP Products at DDN.
Explore DDN’s AI optimized systems and learn more about how we’re accelerating time to AI adoption among enterprises. If you’re ready to talk to one of our AI Storage Experts, contact us today.
