Artificial intelligence has been an integral part of our lives for the past few years. From Amazon's Alexa to Apple's digital voice assistant Siri, AI has been part of our lives for quite some time now. Cloud-based computing has also played a significant role in making our day to day life. Everyday activities like banking, media streaming, emails and much more make use of cloud computing to offer you hassle-free services. If both cloud computing and AI have a lot to offer, imagine a situation where AI can be made use of to improve the cloud infrastructure.
According to Deloitte, companies will accelerate their usage of cloud-based artificial intelligence in the years to come. Earlier days application of AI technologies were limited to companies that are having excellent infrastructure services. Things started to change once cloud infrastructure came into the limelight. In the future cloud will ensure more spending on AI technologies and a better return on investment (ROI) from AI.
Cloud computing allows users to store and access all their pieces of information without needing a bulk storage system of their own. Users can make use of a cloud-hosted server to store their data which can be retrieved whenever necessary. Almost all people make use of cloud-based services in their day to day life even without realizing it. Gmail, Instagram, and other similar apps all make use of cloud services. Security, flexibility, cost savings, mobility, disaster recovery are some of the benefits offered by cloud solutions. Artificial intelligence, on the other hand, is the process of making machines think and work like humans. Machine learning, deep learning neural networks are some of the technologies that are part of the AI. Recent advancements in machine learning and deep neural learning have been a significant factor that led to a massive rise in demand for AI.
Computers, laptops etc. are very much common in the present-day world. Almost everyone has access to these facilities. Even though these devices can perform routine operations at high speed, their computational power is too small to run AI applications proficiently. Cloud computing has got a vital role to play providing these computational power needed to AI applications. Cloud computing helps in making AI more accessible to organizations. More the computational power, the better the performance will be. Machine learning technologies use different sets of algorithms to analyze a vast amount of data. It requires an enormous computational ability to train a neural network on the given dataset. Cloud computing offers the benefit of computational power, thereby putting the entire calculations on the cloud. Combining AI and cloud will help to exploit their benefits as a whole.
Now let's have a detailed look at the benefits of mixing AI and Cloud.
Almost 90% of the data available in the world was obtained in the past few years. Large sets of data are daily uploaded into the cloud, and thus it becomes difficult to manage these data manually. Human errors can also cause data to be misplaced all over the cloud. By making use of AI, it is much easier to retrieve stored information from the cloud. Machine learning algorithms can be used to find out the correct file from the cloud-based on keywords or any word present in the document or even by inspecting images. Moreover, AI tools can help make the cloud storage more efficient by managing the way data is ingested, updated, and managed. One example is by using AI in unison with Alibaba Cloud's Object Storage Service to effectively manage the storage of data in the cloud.
Cloud can be made to self manage by making use of AI technologies. AI technologies can be put into use to automate repetitive tasks, thereby reducing human efforts and time.AI can be used to monitor, manage and fix issues the moment it occurs. The routine process can be automated, and efficiency of the cloud can be increased by making cloud to self-heal itself during the occurrence of an issue. AI tools can be used in unison with monitoring services such as Cloud Monitor to further improve the real-time monitoring process.
Organizations need to have trained resources to analyze vast amounts of data saved in the cloud. By using AI tools such as E-MapReduce, organizations can obtain relevant information with ease from a large set of data. At present, it requires the help of a team of analysts to churn relevant and meaningful data from the cloud. AI can reduce the dependency on humans and thereby cutting the cost to a considerable amount. Even AI can be used to predict things based on the analysis of data, thus helping organizations to create various strategies.
Virtual assistants such as Siri, Alexa etc. are becoming part of consumer's daily lives. Machine learning and cloud computing have helped make the idea of virtual assistants great success. From learning new words to carrying out a conversation, these assistants are making use of machine learning algorithms and cloud to a huge extent. Data stored in the cloud, along with AI technologies, are used by these virtual assistants to respond to the enquiries made by the consumer. As AIs learns, those new data can be stored back in the cloud for later use. Speech analytics, speech-to-text, text-to-speech are some examples where AI and Cloud are used in unison.
Cloud security is another factor that will improve with the use of AI. AI can process information and can detect any issues immediately. Such things can prevent unauthorized access of data from cloud to a large extent. AI can further detect abnormal activities and even block them.
The potential of using AI and cloud in unison is endless, and many data science companies such as Datahut believe in the fusion of cloud computing and AI. Even though AI technology is still in its infant stage, we can expect phenomenal advancements to happen in the cloud solutions soon. Better work experience, better efficiency, more insights, better security, reduced cost are some of the benefits if both cloud and AI are used together.
Sandra Moraes
Content Management Specialist at Datahut
Sandra Moraes is the Content Management Specialist at Datahut. Professionally she is a Data Scientist and researched Data Analytics. She authored on Data Science and Customer relationship.
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