Machine and deep learning applications are all set to bring new workflows and challenges to the presently adopted enterprise data center architectures. One of the key challenges that many enterprises have to answer is the data and the need for preparing a storage solution for the complete infrastructure that satisfies the need to store, manage, and deliver AI demands. The current intelligent applications require infrastructure that is very varied compared to traditional analytics workload that can help organizations data architecture decision will be having a bigger impact on the success of such types of it’s AI projects.
The research firm Moor Insights and Strategy added that while the added solutions in data can cope with machine learning and deep learning, forcing to invent new ways for the data storage. Deep learning requires thinking in a different way about how data is managed, analyzed, and stored. The way many businesses are pursuing that is to think about the required cycle between the storage and computing, one in which storage system is paired with a deep learning computing system. It would have an ability to access and serve up large data sets with extreme concurrency without causing the processing of the elements to stall while they wait for data. Deep learning requires a large amount of data that is continuously fed in the processor without making such a processor wait for the data.
One of the major takeaways here is that serving up data for machine learning and deep learning is very different from any other type of enterprise workload. Managing data for deep learning requires the major adoption that needs deployment of solutions that built for high concurrent functions and even involves multi-dimensional directional performance. The added scale with tiering across a single namespace and simple management through a consistent set of tools that can make data center flexible for deep learning solutions.
