“A baby learns to crawl, walk, and then run. We are in the crawling stage when it comes to applying machine learning.”
- - Dave Waters
Technologies and their interdependencies are paving the way to a future that is smarter and intelligent to fulfill the demand for resources. The advancement in technologies has changed the way people live and is addressing the growing needs of the living making the future intelligent. Since AI is evolving with time, Cloud computing is entering a new mode called Intelligent Cloud.
The world is yet to encounter the advanced stage of Intelligent Cloud and its capabilities. Cloud computing and machine learning, the promising technologies have individually captured attention for their unique compelling peculiarities. And, now from storage solutions to solving complex problems, ML and Cloud computing are together developing with time and taking over the world with a vision to reach a point where there is no need for human intervention.
Machine learning, an application of artificial intelligence (AI) allows systems and machines to automatically learn from data and improve through experiences without being programmed or any human assistance. The benefits that we gain from machine learning solutions are immense. Nonetheless, combining ML with Cloud computing takes automation to another level contributing to a nifty number of applications that are taking over the world with evolution in automation. The scalability and low cost resources are other premiums that come along both the disciplines.
We know Cloud is particularly for computing, networking and storage of data. When Cloud based deep learning comes together with real time Machine Learning, their capabilities increase significantly. Cloud providers like AWS, Google and Microsoft offer Cloud with ML capabilities that require scads of processing power. For example, Amazon Web Services supports machine learning using AWS's algorithms to read native AWS data. Google has supported predictive analysts with its Google Prediction API, and Microsoft provides an Azure machine-learning service.
The ML models are built from the incoming transactional data in the wake of which the patterns in data are then studied to make predictions and recommendations for businesses to flourish. The Machine learning Cloud service technology is used far and wide for all that data the big enterprises have been collecting.
Undermentioned are a few applications of Machine Learning Algorithms using the Cloud
Cognitive Cloud - There are millions of people using cloud and they produce massive amounts of data frequently. The pre-existing unstructured datasets act as a source of information for the machines to learn from and adversely the cloud provides applications with sensory capabilities that are used to perform cognitive functions and make prospective decisions. Adaptability and Natural language interaction are some of the learning characteristics of Cognitive Analytics system and machine learning imposes diverse algorithms to provide influential cognitive capabilities as the upshot.
Virtual assistants - Technology is just making human life easier and so are personal virtual assistants like Apple’s Siri, Google’s Alexa, and Microsoft’s Cortana. These pre-coded voice recognition systems decode human voice, operate devices, write without having to use a keyboard, mouse, or pressing any buttons, or performing commands. Chatbots providing solutions through chat has brought them into focus in the customer service industry eliminating human interaction, hence reducing any errors. Anyhow, Machine learning has the potential to increase the cognitive capabilities of chatbots and virtual assistants as they learn from past conversations to provide better customer assistance. The technology advancement has made it possible for us humans to have a real conversation with the assistants which involves not just chatbots and intelligent VAs answering the queries but they also provide prolific suggestions and recommendations. The intention is to make them as human as possible. Companies now develop AI systems that learn new words and use them in their following future conversations.
IoT Cloud - Gartner predicts that by 2022, more than 80 percent of enterprise IoT projects will include an AI component, up from only 10 percent today. It’s prominent that the Internet of Things development is working towards connecting everything existing on the web. And, the Smart sensors that are embedded on objects and devices present in an IoT ecosystem carve out data that is capable of automatically identifying specific patterns and information like pressure, temperature, vibrations, sound, etc. Cloud services with ML power IoT and enable it to subdue reduced latency, unplanned downtime, and enhance risk management in business operations. They provide IoT devices with efficiency, flexibility, and scalability so that Ml identifies and rectifies problems even before the user realizes.
Top providers of IoT services, contribute in integrating Cloud and ML capabilities with the motivation to bring out the best of these technologies to minimize the saddle of responsibilities that businesses hold today in the competitive market. Many game app developers have also been integrating AI in games to build highly responsive, adaptive behaviors primarily in non-player characters (NPCs) just like humans. Along with making the most to increase the chances of prosperity for any brand.
Machine learning and cloud computing together contribute to making Business Intelligence improved by predicting the future, identifying and resolving mistakes, and figuring out real time anomalies. Business Intelligence tools have evolved over the time and now make conjectures and share insights faster and with distinguished accuracy.
Businesses operations in today’s competitive and resurgent environment have invoked a great demand for real time advanced and predictive analysis based on processed data. Introducing machine learning and cloud computing into business intelligence, makes it smarter and proactive. Performing operations, Decision making, Managing resources are valuable processes for a business and ML with Cloud makes these processes smart driven and give businesses the competitive edge in the industry.
ML Platform as a Service - ML PaaS helps build customized ML models when cognitive APIs go short of requirements. ML PaaS depends on data scientists to provide unique data sets to train the models against them so they are able to execute complex machine learning jobs. The challenges involved in setting up and configuring data science environments are overcome with ML Paas. ML Platform-as-a-service offers pre-configured environments that are used to train, tone, and host the model by data scientists and provide tools to effectively manage the lifecycle of the machine learning model. A couple of examples of ML Paas are Microsoft Azure ML Services, Google Cloud ML services, and Amazon SageMaker.
ML Paas brings flexibility in machine learning model development and deployment which most of the software development companies in India are focusing currently by bringing together the techniques of CI/CD and ML model management.
Reality-as-a-service - Technologies like Drones and location providers capture a lot of data and at a great frequency. Aerial imagery-as-a-service delivers different resolutions visual images to the Cloud providing easy access of aerial images from any device. This is known as Reality-as-a-service (RaaS). RaaS with ML makes automatic detection of stationary objects without human intervention possible analyzing heights, width, weight, etc. Applications lie in autonomous driving, smart cities, mapping, architecture, construction, solar and engineering industries and so forth.
According to a recent survey by Evans Data Corp, 6.5 million developers are currently using some form of artificial intelligence (AI) or machine learning (ML), and another 5.8 million plan to start using artificial intelligence or machine learning within six months. Given that there are more than 22 million developers worldwide that means a majority (around 56%) are either using these technologies now or will start using them soon.
All Businesses work on data, all because of Cloud computing. And, yes it’s those chunks of unmanageable data in Cloud that makes it possible to create machine learning algorithms that are reliable and models that are powerful. ML algorithms operate as a thrust to drive services like Marketing, Customer support, Social media, Business Intelligence, Fraud detection, and more by making machines learn patterns and predict insights. As a result of which, productive decisions are made backed by factual analysis making sure that the future of business is procured.
Here you will know about how AI and ML Can Win Election:Artificial Intelligence and Machine Learning are ushering in a new era of politics.
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When hearing ‘AI for Customer Service’ most would immediately think this is going to be ‘yet another article about chatbots’. No wonder. Chatbots have been one of the hottest topics recently. However, our experience shows that AI can be applied to many other areas of Customer Service business as well. And those possess even more potential to boost your business.
Best Customer Service Chatbots To Relieve 20-30% Of The Load
As the hottest topic in 2017, AI support bots were expected to be a game changer. And even though the ebullience faded a bit when it became clear that even best customer service chatbots still have a lot to learn, a properly designed and trained chatbot can substantially relieve the load on 1-st line support.
As statistical data show, 87% of chatbot users worldwide consider them efficient or very efficient in resolving issues.
As you see AI and ML benefits for customer service are already obvious. AI and ML are definitely changing the rules of the game in the customer support industry and the use of AI/ML technology in this industry will only grow.
However, AI and ML are rather additional tools, which can amplify but not replace traditional customer service automation software and savvy approach to automation. No matter how intelligent your systems are – they are just the reflection of your organization and your processes, which should never be neglected.
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