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AI and machine learning
challenges in the availability and preparing of data . A business cannot become data-driven , if it doesn ’ t understand the information it has and the concept of ‘ garbage in , garbage out ’ is especially true when it comes to the data used for AI .
With many organisations still on the starting blocks , or having not yet entirely finished their journey to become data driven , there appears to be a misplaced assumption that they can quickly and easily leap from being in the process of preparing their data to implementing AI and ML , which realistically , won ’ t work . To successfully step into the world of AI , businesses need to firstly ensure the data they are using is good enough .
AI in the data centre
Over the coming years , we are going to see a tremendous investment in large scale and High-Performance Computing ( HPC ) being installed within organisations to support data analytics and AI . At the same time , there will be an onus on data centre providers to be able to provide these systems without necessarily understanding the infrastructure that ’ s required to deliver them or the software or business output needed to get value from them . We saw this in the realm of big data , when everyone tried to swing together some kind of big data solution and it was very easy to just say we ’ ll use Hadoop to build this giant system . If we ’ re not careful , the same could happen with AI . There have been many conversations about the fact that if we were to peel back the layers of many AI solutions , we ’ ll find that there are still a lot of people investing a lot of hard work into them , so when
it comes to automating processes , we aren ’ t quite in that space yet . AI solutions are currently very resource heavy .
There ’ s no denying that the majority of data centres are now being asked how they provide AI solutions and how they can assist organisations on their AI journey . Whilst organisations might assume that data centres will have everything to do with AI tied up . Is this really the case ? Yes , there is a realisation of the benefits of AI , but actually how it is best implemented , and by who , to get the right results , hasn ’ t been fully decided .
Solutions to how to improve the performance of largescale application systems are being created , whether that ’ s by getting better processes , better hardware or whether it ’ s reducing the cost to run them through improved cooling or heat exchange systems . But data centre providers have to be able to combine these infrastructure elements with a deeper understanding of business processes . This is something very few providers , as well as Managed Service Providers ( MSPs ) and Cloud Service Providers ( CSPs ) are currently doing . It ’ s great to have the kit and use submerged cooling systems and advanced power mechanisms but what does that give the customer ? How can providers help customers understand what more can be done with their data systems ? How do providers differentiate themselves and how can they say they harness these new technologies to do something different ? It ’ s easy to go down the route of promoting that ‘ we can save you X , Y , Z ’ but it means more to be able to say ‘ what we can achieve with AI is .. X , Y , Z ‘. Data centre providers need to move away from trying to win customers over based solely on monetary terms .
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