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Effects of Integrating Machine Learning in Cloud Computing

Machine Learning in Cloud Computing
Nowadays, most businesses across the globe are leveraging cloud technology and its advanced natives, such as machine learning and artificial intelligence (AI), to pave their path towards success. As per Analytics Insight, Machine Learning would record revenue of US$80.3 billion by the year 2023, with a CAGR of 33.6% from 2020. This article, explains the effects of integrating machine learning in cloud computing and how it helps make future-proof decisions, create innovative solutions, and drive organizational success.

Machine Learning (ML) is one of the subgroups of artificial intelligence. It helps infer effective business decisions to attain digital presence in the market by providing valuable insights from disparate organizational data.

“AI works effectively because extremely high volumes of high-quality data drive it. So, identifying and collecting data, and making that data meaningful, is an integral step in the machine learning process.”

Jaime Punishill, CMO at Lionbridge said during an interview with Media 7.


Importance of Merging Machine Learning in the Cloud and Some of the Enterprise Case Studies

Machine Learning utilizes the resources of the cloud to optimize the industrial sectors. On the other hand, cloud computing provides scalable and cost-effective resources to leverage a considerable amount of data for processing to run ML-enabled systems efficiently. As a result, combining machine learning with cloud helps to optimize the capacity of both.

Let us quickly run through some of the stimulating effects of merging machine learning in cloud computing.

Detect and Protect Business Ecosystem

According to the IDG Survey, almost 80% of IT security leaders believe their organizations are susceptible to cyberattacks despite augmented investments made on IT security to accommodate the advanced distributed IT framework. Today, most business leaders are aware of security threats because of various cloud-native and web-based modern applications. In addition, by merging machine learning in cloud computing, companies are developing intelligent security applications to protect organizational data from vulnerable malpractices.


How an Advanced Approach by McAfee Endpoint Security 10.5 Helped a Leading Insurance Company Safeguard its Sensitive Data?
 

For an insurance company, securing their customers’ sensitive personal data without compromising customers’ experience is imperative. And there lies the primary challenge of any IT security measure, where the traditional silos approach doesn’t work anymore. In this case, you need robust and automated solutions powered by advanced technologies, such as machine learning. Eventually, to solve this, McAfee deployed McAfee Endpoint Security version 10.5, and as a result, the company’s IT help desk started receiving 80% lesser tickets. In addition, the insurance company is now enabled with cloud-based Real Protect machine learning behavioral analysis technology that helps enhance overall security capabilities and stand against the rising risk of security breaches.

Cloud-Based Cognitive Technology

Integration of machine learning in cloud computing makes cloud data the source of ML algorithms for cloud-enabled businesses. ML algorithms utilize the cloud data and modify the cloud archetype to cognitive computing. Cognitive computing technology is one of the emerging trends. Businesses are inclined to deploy cloud-based cognitive technology because it helps to grow their revenue, enhance operational efficiencies, and cater to real-time use cases in a cost-efficient manner.


How Does IBM’s Watson Help Banking and Financial Sectors?
 

IBM’s Watson is one of the ideal examples of cognitive computing. Assisting the banking and financial sector, IBM’s Watson is a question-answering system, which receives unstructured data in the form of questions and provides humanized answers. Furthermore, Watson can differentiate its limits and route it to respective resources whenever human intervention is required. For example, IBM Watson has helped the Royal Bank of Scotland develop an intelligent assistant proficient in handling 5000 queries in a single day.

Predictive Analytics

Predictive analytics uses predictive models that are typically machine learning algorithms helping to make accurate predictions of your business outcomes. Playing a crucial role in cloud computing helps to optimize cloud infrastructure, take proactive measures to envisage downtime or infrastructure performance issues. Further, predictive analytics play an intrinsic role in merging the structured and unstructured data from diverse and distributed networks in multifaceted cloud environments.


How WNS Helped a Globally Acclaimed Hotel Chain to Retain its Timeshare Members by Leveraging Predictive Analytics?
 

For hotels, it is crucial to retain their timeshare members. However, experiencing a high attrition rate among its timeshare members, followed by nonrenewal or membership cancellation, started denting the hotel chain's reputation.

WNS recommended an approach based on the principles of the predictive analytics approach for a better understanding of the behavior of the members. They created complete member profiles based on demographics, duration of membership, and transactional patterns. Implementing statistical analysis to generate probable attrition scores for every member helped to divide members into high, medium, and low attrition groups. Further, identifying the attrition drivers by deploying a logistic regression model was also a part of this exercise. All these drivers helped to predict the members' behaviors in the future.

As a result, the insights empowered the hotel chain to employ marketing campaigns targeted towards the specific audience with special promotional offers to arrest the attrition of their timeshare members.

Internet of Things (IoT)

Internet of Things is described as a network through which multiple devices (read 'Things') are interconnected via the internet. IoT is adapted by technology experts worldwide. Various industrial sectors embrace the utilities of IoT devices. Further, when hosted on a cloud platform and leveraging machine learning in the cloud, IoT provides impactful real-time insights.


How Did Medium One Leverage Machine Learning to Enable Cloud-Connected Industrial Pressure Sensors?
 

The legacy industrial sensors couldn't generate automated real-time alerts to monitor, detect irregularity, or forecast key events. Medium One’s environment helped the customer in making data sensible to unlock its hidden insights. Medium one helped develop a machine learning algorithm for real-time predictions and alerts with historic cloud data. It enabled the cloud-connected industrial pressure sensors to correlate events smartly and monitor remotely.

Chatbots and Virtual Assistants

Chatbots and personal assistants are innovative examples of machine learning in cloud computing collaborations that dominate personal and corporate ecosystems. Intelligent cloud-based virtual assistants like Siri, Alexa, Cortana interact with you just like any other human being and perform several operations, as per your command. These chatbots are AI-enabled, operate on machine learning algorithms in the cloud computing framework. Further, they use natural language processing technology, predictive analytics, and sentiment analysis to learn from the inputs and engage in real-time conversations.


How Oracle Intelligent Bots Reduced Call Center Wait Times?
 

During the release of senior school exam results in the summer, the inquiry center of the University of Adelaide received massive traffic from the existing and prospective students. A significant part of that traffic was to enquire about their grades and admission facilities, respectively. The university appointed Rubicon Red, an Oracle cloud consultancy, to build a chatbot based on artificial intelligence (AI), machine learning, and natural language processing to meet this surged demand. The bot is supposed to handle the first line of inquiry when calls come in, relieving human agents' load. Rubycon Red leveraged Oracle Intelligent Bots to create an intelligent chatbot. The deployment was a success that resulted in 40% reduced traffic and 97% less wait time on the university's inquiry service (call center). 

Final Thoughts

The merger of machine learning in cloud computing enables organizations to leverage their massive organizational data in deriving valuable data-driven insights and accurate predictions by analyzing the trends and patterns of the data. Machine Learning helps businesses to understand their target audience, automate their business processes and develop advanced products per market demand. As a result, it drives the success of a company and helps them to stay competitive.

Frequently Asked Questions

How is Machine Learning Used in Cloud Computing?

Cloud-enabled businesses can leverage the vast data and utilize advanced machine learning technology to determine, compute and predict valuable futuristic insights about their business. As a result, it helps to scale the efficiency of the cloud within a cost-effective reach.

Is Machine Learning Important for Cloud Computing?

Machine Learning leverages cloud data and derives insightful information out of it. It computes the data to provide future forecasts and helps the businesses to take necessary actions on it. As a result, adopting machine learning applications are crucial for cloud businesses.

What are the benefits of Machine Learning on the Cloud?

Businesses can experiment with their processes using machine learning on the cloud and then scale up as demand increases. Further, the pay-per-use model makes a cost-effective solution to the company without unplanned expenses.

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