AI Storage leading to performant usage
AI storage solutions; Improving business performance
AI storage solutions are now making the technological scene and here to stay. They serve to help with load leveling, predictive data usage for data retrieval optimization and cybersecurity. Artificial intelligence (AI) is essentially a weighted node decision making matrix that is iteratively pre-trained is trained and optimized through changing weighting values until the matrix consistently guesses the correct answer based. These matrices are as smart as the number of layers (or filters) they have and the acceptable error level they are trained to.
How to identify a good AI Storage Solution
An AI decision making matrix if not trained enough with a large enough data set will provide a lot more error than an AI that has used a larger dataset. All AI is fallible just like humans, but if the error tolerance is low enough error may be deemed as acceptable for the purpose of the AI system. For instance, an AI system could be trained to guess correctly 99.9% of the time however there will be instances where 0.01%, a failed result is possible. The best ai storage solutions will have the smallest error values than competitors.
The more layers AI storage solutions have the more filtering and decision making occurs however this also increases computation time. For instance, a chess AI could have more layers added to help it predict more moves ahead and counter accordingly; however the computation time becomes exponential limiting its performance in relation to time. A self-driving car also has this challenge so more sensors and layers are not necessarily good for real-time usage or error tolerance. This is something to remember when purchasing AI storage solutions.
Big data and AI Storage Solutions
Performant AI storage solutions will have a lower error tolerance and a good trade off between filtering layers and response time, however what does this mean in context of big data? Big data cloud storage solutions are one of the ideal use cases of AI storage solutions where manual performance optimization is not acceptable due to the scale of the system and the different transaction that may occur. Big data cloud storage solutions need to from one instance to another, change load leveling, resource overheads dynamically and have a unified approach to how the system deals with device failure.
As big data cloud storage solutions store much larger file sizes for things like FEA models, point cloud data for reverse engineering and real-time AR technology very quick changes to data sizes can occur and without system balancing there is a much higher risk of reaching a capacity limit due to provisioned overheads not meeting requirements. AI storage solutions look after all these worries in real-time allowing administrators to focus on high-level tasks and not fire-fighting.
An additional benefit of AI storage solutions is that they can be used with NVMe hardware allowing for vastly improved data transactions through read and write occurring at the same time in addition to having a large bandwidth.
Summary
AI storage solutions combined with NVMe protocols will allow businesses to accommodate the change to larger data sets, allow administrators to divide big data across different AI storage solutions and these likewise managing content according to top-level data policy. Essentially the AI acts as an abstraction layer that frees up the administration and essential for big data cloud storage solutions that would require a lot of micro management from a larger administration team working at a less performant pace. Big data cloud storage solutions are easy to scale and balance when AI is used to control routine repetitive tasks and additionally benefited from NVMe compatible hardware.
