Why Cloud is important for AI Initiatives?

Businesses are driven by data and they are looking to artificial intelligence (AI) for the future of data management and analysis. In many ways, AI and cloud computing make the perfect match for data-driven innovation. AI’s cognitive capabilities and machine learning thrive on large volumes of data, which become scalable and quickly accessible in an environment. Businesses that leverage AI in the cloud set themselves up with a huge critical advantage.

In the field of computer science, one is often overwhelmed by the number of new emerging technologies. In the light of big names like AI, Deep Learning, AR (Augmented Reality), IoT, and Blockchain, the other ones are often unnoticed. One such technology that has silently become a common word amongst computer programmers and businessmen is “Cloud”. Cloud computing has created massive opportunities, an eCommerce platform such as Amazon, earns most of its revenue (more than 50%) from AWS – its own cloud computing service. Cloud computing is the foundation of PaaS or Platform as a Service – a new B2B service.

The rise of mobile increased the use of Cloud computing and reduced the reliance on traditional computers. Have a slow laptop? No problem, spin up a fast Virtual Machine (VM) on Microsoft Azure. Thinking of utilising the vast amount of data provided by IoT sensors? Send them to IBM Watson Cloud and Machine Learning (ML) algorithms will have enough food to digest to create new products, revenue, and value.

But the question the article addresses are, what role does the cloud play in game-changing technology such as AI? This begs two questions – why should I care if something can impact AI? And can the cloud really impact something as great as AI? 

The answer to the first is slightly obvious – according to Accenture research, 85 percent of business and IT executives anticipate making investments in AI-related technologies over the next three years. So, AI is important.

But AI alone is too complicated, process-intensive, and has too many needs to make it this resourceful. It turns out, that cloud has an answer to the problems of AI and this is the answer to the second question. An article written by IBM in 2016 said: “That next big shift is the fusion of artificial intelligence and cloud computing”. AI and the cloud are a perfect key-lock combination as both improve each other and the services they can provide. But how does the cloud impact AI?

Several firms adopt cloud-based services and are leveraging SaaS (Software as a Service) & PaaS to execute and deploy AI-infused cloud results. Analysing how AI and the cloud are used together today, one may classify the integration into two major groups:

1. Cloud Machine Learning (CML) platforms such as AWS ML, Azure ML, and TensorFlow (Google Cloud ML) power the creation of machine learning models.

2. AI cloud services: Using AI platforms for businesses like IBM Watson, Google Cloud Vision, Microsoft Cognitive Services, or Natural Language application programming interfaces, that allow abstract complex AI capabilities via an application programming interface (API) calls.

According to an Intel paper titled “The Intelligent Cloud”, the current “revolution” in Artificial Intelligence is the direct result of three key enabled breakthroughs. They are: the emergence of affordable parallel processing; the availability of Big Data; and access to improved (Machine Learning) algorithms

Prior to cloud services, most AI work was isolated and expensive due to huge data, software, and hardware requirements of the algorithms; but the economics of the cloud now enables machine learning capabilities (facial recognition, translating languages, etc.), in a highly accessible manner. 

Can the Cloud meet the massive processing and data storage requirements of AI at a cheaper cost? One of the key technological advancements in this area is the introduction of Graphic Processing Units (GPUs). GPU provide increased computing power, enabling robust management of big data requirements and algorithms that AI system needs to work on a real-time basis. 

According to a Nvidea blog, neural network training is 10 to 20 times faster using GPU compared to CPU. Built-in agility and quality of performance is the perfect example of leveraging AI and the Cloud together, to drive rapid innovation.

Turning the focus to Machine Learning algorithms, or more broadly Data Sciences – although marketing companies and banks use the cloud for scalability and automation, most companies do not leverage the cloud to improve customer engagement – i.e. making the most of every customer interaction across all touchpoints.  As per Wikipedia, “a touchpoint can be defined as any way a consumer can interact with a business, whether it be person-to-person, through a website, an app or any form of communication”. 

Banks continue to struggle to get a single view of their customers, especially due to mergers and acquisitions. In other words, data from one entity of the bank does not align with the data from another entity of the same bank, which is a side effect of a merger/acquisition.  Beyond customer data, the issue impacts digital transformation due to the presence of siloed data. It’s becoming increasingly difficult to do this in the siloed manner that so many of us are working in. We have data from online and in-store sales, email, SMS, and social media campaigns as well as analytical data all sitting in separate systems.

Cloud computing can help to solve these problems. Used in conjunction with big data solutions, this makes it easier to solve data quality and aggregate customer data in one place, in real-time. The combined solution enables banks to generate a single, complete and 360-degree view of the customer, by merging identity and behavioral data, online and offline patterns, and customer service. Data aggregation will facilitate the prediction of customer needs for improved cross-selling, customer-centric marketing offers, and personalised products. 

However, the APIs provided by cloud providers to offer AI services, still require a complex integration process, since they are only a building block of an AI solution, not a software-as-a-service. These APIs still require complex integration to make products enterprise-ready. While an ever-increasing number of companies are turning their operations to the ecosystem, to manage the high complexity that comes with bundling these packaged AI services into a solution to achieve faster results, advancement in cloud services will perhaps make this integration even easier.

AI technology is now available for your businesses, be it a startup or established businesses. This is not a myth or an idea, instead, this is happening and truly in demand. The best way to start the journey using AI & Cloud is to adopt the technology by initiating a proof of concept (POC) and assessing its benefit to the business. These both are digitally converting the way we interact with the world.

Data Pillar AI and Cloud services enable you to put your AI-centric vision into practice so you can respond faster to market changes and rapidly advance new ideas from concept to production. We can help you transform your business by leveraging Cloud and AI, so you can advance your business and change the world with AI. Contact me if you are going through digital transformation and need our support.  

Why business should focus on Data Accuracy for a powerful AI Predicting system? Watch this space for my next article focusing on this area!!

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