Deep learning and artificial intelligence (AI) are among the biggest industrial trends of recent years. This increasing dynamic is undoubtedly closely linked to the emergence of public cloud providers such as Amazon Web Services (AWS) . Google Cloud and Microsoft Azure . While machine learning was once out of reach for many corporate budgets, this technology has now become affordable for SMEs thanks to cloud computing. But what advantages does a public cloud actually bring to machine learning? And how do the various providers in this area differ from each other?
This blog article attempts to address
These questions. Advantages of machine learning DB to Data in the cloudUltimately, machine learning is always about recognizing patterns and regularities based on existing data sets using algorithms and statistical models. These insights gained from the data can be generalized and used for new. Problem solutions or for analyzing previously unknown data. The most important prerequisite for learning systems and corresponding algorithms is sufficient.
Computing capacity and access to as much data as possible, i.e. machine learning is requiredas much computing capacity as possibleas. Much data as possible as short a time as possible to be able to derive as many valid and generalizable patterns and laws as possible .
Computing capacity can quickly become
Many machine learning projects TH Phone Numbers often fail due to the fact that a lot of money has to be invested in a powerful server infrastructure, which ultimately can only be used to its full potential sporadically. And this is exactly where public cloud services come to the rescue!
With cloud computing, the service provider provides various resources such as applications (Software-as-a-Service, SaaS), computing platforms (Platform-as-a-Service, Paas) or a complete IT infrastructure such as storage or servers ( Infrastructure-as-a-Service (IaaS) is available to the general public via the Internet.
