Censys: The World of Attack Surface Management
Machine learning (ML) is seeping into all areas of enterprise applications. In particular, organisations are using ML to improve customer experience, security, business intelligence, and much more. However, an asset so powerful does indeed come with its own setbacks. For a feature that the user may consider simple, such as the 'Recommended for you' on retail websites, the reality is quite the opposite. Alone, ML requires a significant amount of processing power and storage to analyse the data. This is quite the hindrance for organisations who want to take advantage of ML's capabilities.
Combining with the cloud
You really have to give it to the cloud; right now, it seems like the answer to just about everything! Today, organisations are moving in a flurry to take advantage of cloud-based ML services from the three biggest public cloud providers: Google, Microsoft, and Amazon Web Services. Why? Well, to begin with, cloud is available to anyone. Even in terms of cost, cloud-based solutions are cheap, not only to operate, but for storage too. In particular, businesses can take advantage of the cloud's pay-per-use model to keep costs down. This is especially important for companies who are still trialling uses for ML, as it allows them to experiment rather than commit. Therefore, cloud is a universal solution within the reach of anybody's enterprise; all you need is access. Furthermore, as ML is typically new territory for most organisations, ML in the cloud brings it to the fingertips of employees regardless of their artificial intelligence expertise. Using the cloud also eliminates the hardware and software that bogs down ML initiatives. Instead, enterprises can swap in the cloud for more intelligent solutions. Thus, the low entry barriers and low costs associated make the cloud an especially attractive asset for enterprises. It seems that the mantra of today is 'if in doubt, go cloud'.
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