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Artificial Intelligence and Cloud Computing: A blog about AI and cloud computing.



The role of Artificial Intelligence in Cloud Computing


For the last ten years, AI, especially Deep Learning (a subset of AI), has been on the rise after

two successful events: First, IBM announced the Watson super system which defeated several

Jeopardy multiple-time winners, namely Brad Rutter and Ken Jennings on February 14–15 and

AlexNet, the name of a convolutional neural network (CNN), competed in the ImageNet Large

Scale Visual Recognition Challenge on September 30, 2012, and achieved the top in this

challenge. Based on various market research, almost all institutions in different industries have started to invest in AI and they will increase investments in AI for different use-cases in

upcoming years. Future jobs related to AI will be needed more based on the World Economic

Forum’s ”Future of Jobs” report (October 2020). Moreover, according to Glassdoor's online

employment company, data scientist has been called one of the best jobs in the USA last 3

years.

There are many examples of how AI and cloud technology have become entwined in our daily lives, such as through the use of digital assistants. On a larger scale, this blend of resources makes organizations more efficient, strategic, and insight-driven.

Artificial intelligence has many facets, such as text analytics, machine language translation,

speech, and vision, that can be accessed by developers and implemented into development

projects.


Cloud management: AI can monitor core workflows. Business IT teams can, therefore, focus

on higher-value strategic activities while AI manages routine processes to maximize cloud

efficiency. It's predicted that public and private clouds will soon rely on AI for not only monitoring

and managing but also self-healing.


Data processing: Cloud computing solutions employ AI methodologies to manage large data

repositories. Data management, updates, and consumption are significantly impacted by

AI-driven data streamlining. Identifying high-risk factors and providing real-time data to clients is easier for institutions such as those in the financial sector and customer service industries.

Dynamism in Cloud Services

This point somewhat expands on what we have already mentioned above. By now we are all

aware of what AI can do in terms of managing and monitoring processes. But it can go a step further from just analysis and actually turn recommendations into actions to optimize your cloud best practices.

Rapid business transformation is a result of the merger between AI and cloud computing.


So far we have discussed how AI helps us in cloud computing. Cloud computing in return also

favors AI and returns the compliment.


Though artificial intelligence started much earlier than cloud computing, cloud computing and its technologies have improved AI very much. Cloud computing has been an effective catalyst.


Cloud delivery models


IaaS (Infrastructure as a Service) helps AI practitioners have an infrastructure environment

easily, including a CPU, memory, disk, network, and operating system, so they don't lose time

waiting for infrastructure teams to prepare it. Furthermore, cloud providers began providing GPU resources later on.


Using PaaS (Platform as a Service), AI practitioners can easily create new generation

applications by using Jupiter notebooks and data catalog services.

SaaS (Software as a Service) allows users to consume AI services within applications such as

CRM and payment systems.


Talent/Skill Availability


There have been independent AI engineering bachelor's and data science master's programs offered by different universities, although classical AI courses and education have been available in universities, especially as part of computer engineering, computer science, and applied mathematics. Additionally, well-known universities offer specialized data science courses. Platforms such as Kaggle, and CrowdANALYTIX run on a cloud environment and provide developers an opportunity to collaborate with like-minded people to optimize to compete with them to build deep learning algorithms. These platforms being available to everyone without any fees or subscriptions it has made the playing field for everyone and has been built upon the very first fundamental of the Internet that knowledge and information should always be free.


DevOps

The DevOps approach combines software development and IT operations. The term was first

used at a conference with the same name. DevOps addresses the application development

lifecycle, not the Data Science life cycle. ModelOps was coined by Gartner in 2018. Before

ModelOps, MLOps (Machine Learning Operations) was used as an extension of DevOps.


Cloud providers and analytic firms are always looking for ways to improve their data science and machine learning platforms. They are constantly adding new features and services to their offerings, and many of these are available on different cloud providers. Some data science platforms and AI API services are also available on-premise, but the majority are cloud-based. It seems that cloud providers and analytic firms will continue to improve these services on the cloud platform.


Different analysts and technology companies have different predictions for how artificial

intelligence will be used in different industries in the future. The way that cloud delivery and

cloud computing models develop will have a big effect on what sorts of AI use cases become

practical. In addition, edge computing (which gives devices the ability to do some processing

even when not connected to the internet) will open up new possibilities for AI applications, since

organizations will be able to do more with the data they have on hand. Finally, quantum

computing is expected to give a boost to AI development, particularly in the area of machine

learning.


If you need help with your Software engineering requirements, Please contact 'Hello@fusionpact.com'


Know more about us by visiting https://www.fusionpact.com/

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