From on-premises data centers to cloud, IT operation has evolved exponentially over the last decade with the help of cloud computing services offered by companies such as Amazon, Microsoft, and Google. Over the course of time they have removed much of the heavy lifting related to installing data centers, servers, managing networks, storage, and more. The increasing adoption of cloud and the emergence of artificial intelligence (AI) are allowing companies to use intelligent software automation to make decisions, predict issues, and provide diagnostic information to reduce the operational overburden for engineers.
AI has changed the way data is stored,processed and analysed. Now, the cloud is not just a data warehouse but, it is an ‘intelligent storehouse. We can say that one of the reasons for the widespread usage of AI related technologies is an advancement in the field of cloud computing. Due to cloud computing, even small startups are capable of getting servers of huge processing power and high end GPU easily. This tremendously led to the growth of AI related technologies. Cloud computing has become an integral part of several companies because of benefits it offers to the new world. For about 90% early adopters of cloud claim that cloud computing will play a better role in artificial intelligence in upcoming years. Cloud computing has helped organizations in many ways; from getting rid of the heavy hardware to up-scaling and down-scaling whenever required, resulting in savings for the organizations.
There are two main features provided by cloud computing for running AI systems effectively. These are scalable low resources and high power processing to handle large amount of data. These two features are great advantages for machine learning. We will discuss some features that cloud offers for AI-related services:
Cognitive Cloud computing:
With millions of people using the cloud for computing, storage and networking, millions of processes will happen every day. These create more and more data. Thus they provide information for the machine to learn from. The whole process will provide applications in the cloud with high end capabilities. The applications will be able to perform cognitive functions and make better decisions. Some examples of cognitive computing in the current market are IBM Watson, AWS IA and Microsoft Cognitive APIs. As time elapses, we can expect that these systems will take over hospitality, healthcare, business and even personal life.
Having a conversation with a computer might looks like science fiction even a few years ago. But now, most of us use chat bots for a variety of tasks. An AI based chat bot is a computer program that uses AI to have a conversation with humans. Users can ask questions, make requests and respond to chat bot questions and statements using natural language. We can feed text input,audio input or both to chat bots.
The use of chat bots have increased widely now days. By using various human interactions as a source to learn, chat bots are partially prepared with everything that a beneficial conversation requires. However, there are still things that can make them lag behind from actual human interaction. Despite several advantages of chat bots such as reduced cost, 24X7 availability, multiple customer handling, self-learning and updating etc. While discussing about personal assistants or chat bots, the first thing coming to our mind will be probably google home, amazon Alexa or Siri. Such personal assistance tools can have a great impact on human life. Fantasies of owning computer systems like those in science fiction can become a reality. There are several cloud services which helps for this by their services like amazon Lex, Azure bot services etc.
Availability of high end GPU
CPUs are designed for more general computing workloads. Graphical Processing Units (GPUs) in contrast are less flexible, and are designed to compute in parallel with the same instructions. Deep Neural Networks are structured in a very uniform manner such that in each layer of the network, thousands of identical neurons perform the same computation. Therefore the structure of a deep neural network fits quite well with the kinds of computation that a GPU can efficiently perform.
So GPU’s are important in case of deep learning. GPU is the computing platform that can deal with large amounts of data with better processing power and act as a computational engine for the new era of AI. Now, bringing all the power of GPU-accelerated deep learning and AI to your data in the cloud opens up a world of possibilities. Cloud computing makes AI more popular because most types of hardware people use (laptops, smartphones) does not have enough computing power to run many AI applications proficiently. Cloud allows to overcome these problems. Also cloud technology offers machines with high end GPU which they can pay on an hourly basis whenever necessary. This helps companies reduce their cost to a certain extent.
Now, bringing all the power of GPU-accelerated deep learning and AI to your data in the cloud opens up a world of possibilities. Cloud computing makes AI more popular because most types of hardware people use (laptops, smartphones) does not have enough computing power to run many AI applications proficiently. Cloud allows to overcome these problems. Also cloud technology offers machines with high end GPU which they can pay on an hourly basis whenever necessary. This helps companies reduce their cost to a certain extent.
Infrastructure optimization tools
Artificial intelligence (AI) workloads are consuming even greater shares of IT infrastructure resources. AI is also taking up as an embedded component for monitoring, managing, securing and controlling IT infrastructure. With rise of AI there arose many infrastructure optimization tools. Due to this there occurs rise in self-healing, self-managing, self-securing, self-repairing, and self-optimizing applications. Such tools are still under development in cloud and we can expect more in future. Because of the ability of artificial intelligence to perform continuous analysis on log, anomaly detection, predictive maintenance, root cause analysis etc, managing complex multi-clouds may become infeasible or expensive for many organizations. AI will continue its technology innovation to continuous integration and delivery.
Increased Productivity With Cloud Computing
Hard disks or local storage devices in local premises are a long back story. Now everything is hosted in cloud. AI will further expand the scope of IT infrastructure automation. In future we will see intelligent infrastructure powered by sophisticated algorithms using technologies such as machine learning (ML) and deep learning. Machine learning will also help in giving way to intelligent CI/CD pipeline.This can change the view of continuous integration and continuous deployment. Some scopes of AI in IT infrastructure includes
Forecast the metrics : Predict when a metric will hit a particular threshold.
AI based devops analytics: Helps to produce good analytics on devops activities.
Cost optimization: Helps to predict the total cost of a user account in cloud.
Self healing systems: Systems that can heal themselves based on errors it generates using techniques like anomaly detection. We can hope the arrival of such operating systems in future.
The advent of cloud computing in AI related services does not end here. It’s scope will continue to flourish as time moves forward. Thanks to cloud technology, AI is now a reach for medium sized companies and small startups. AI is now under evolution stage. It is facing lot of challenges and has not yet reached its complete state. So we can expect that as time progresses more and more cloud services will upgrade more and will create more impact on AI.