Artificial intelligence in cloud computing
Artificial intelligence is now used almost everywhere it is necessary and possible – so AI is also an interesting solution in the cloud.
Cloud computing enables users to store and manage data efficiently while offering benefits such as data security, encryption, regular backups and hosting of cloud applications. Combine these structures with artificial intelligence (AI) and you have the ideal conditions for machine learning (ML): large data sets that can be applied to algorithms. The more data fed into a model, the better the predictions and the higher the accuracy.
When merging AI and cloud computing, it is important that it not only enables the user to store data, but also to analyze it and draw conclusions. Chatbots are a now common example: their AI-based software simulates conversations with. Cloud-based services store enormous amounts of data, which the chatbots can use to learn and grow.
Those aiming for such a fusion of AI and cloud computing should note that advanced computational methods require a particularly powerful combination of CPU and GPU. Cloud service providers such as Centron enable their customers to perform such processes by providing suitable virtual machines as part of specific IaaS (Infrastructure-as-a-Service) offerings. Such services can also help with the processing of predictive analyses, among other things.
Challenges
- Data storage. All data must be stored and securely encrypted. There are certain rules that specify that cloud service may not be used.
- AI Security. Encryption, firewalls and security protocols of software, hardware and data must be considered.
- Integration. It must be clarified whether (and how) AI applications can be integrated into existing applications or systems.
Advantages
- Increased data security. There are already several AI-based network security products on the market to mitigate potential data breaches, close security gaps, prevent data theft, and prevent accidental loss or corruption of stored data.
- Savings. Companies can use AI to leave the traditional data center and move to the cloud – where storage is only purchased when it is needed.
- Reliability. Cloud computing services are always available. Even in case of damage or problem with the system, it is easily accessible from other servers.
- Agile development. The flexibility of cloud computing enables shorter development cycles.
- Redesign of the IT infrastructure. The demand for an optimized working environment has never been greater. Cloud computing allows companies maximum flexibility and scalability.
Disadvantages
- Data privacy. Companies should urgently create privacy policies and protect all data when using AI in cloud computing.
- Connectivity concerns. The systems require a permanent Internet connection. If this is too slow, the advantages of cloud-based algorithms for ML quickly become invalid.
- Error probability. Working with AI currently still holds enormous potential for error. Trust and control must be built up.
Conclusion
AI helps IT teams work deeper and change IT infrastructure quickly by providing automation and other capabilities. However, there is a lot to consider when implementing the algorithms as well as managing the systems. To avoid problems here, companies should either build up appropriate expertise or buy it in in the form of specialists or experienced service providers.
