When Cloud Meets Machine Learning

Author/Christine|Translator/Maggie, Bonni|Editor/Jessie

Cloud-computing is gradually replaced by cloud systems integrated with big data. Even though cloud vendors still make major profit by sharing a pool of configurable computing resources (e.g., networks, servers, storage, applications, and services), machine learning has become the new “battlefield” in cloud services.

Forbes’ article (see Reference) points out five cloud services derived from development of machine learning:

1. Computing:

Cognitive computing refers to hardware or software that simulates the functioning of human brains/minds. Cognitive computing applications allows computers to recognize (see), interpret human languages (listen and speak), and even make decisions. Corporations from different industries, including insurance and finance, are going to embrace this technology to strengthen their IT infrastructures. Google Cloud, Amazon AI, IBM Watson, Microsoft Cognitive APIs are solutions available in current market.

2. Bots as a Service:

Application of interactive Bots in business are emerging since various types of apps are getting familiar with mobile users. Bots embedded with conversational functions have been around us in our daily social media apps, such as Facebook, WhatsApp, LINE, and WeChat, etc. Though the idea of interactive Bots was found early in the era of Yahoo! Chat, with machine learning, developers are able to train Bots to respond naturally based on the context and chat history. And platforms like API.ai, IBM Watson Botkit, and Microsoft Azure Bots as a Service are examples of interactive Bots.

3. Internet of Things:

Although discussion over Internet of Things, or IoT, have existed for over two decades, the evolving cloud computing technology keeps refining the trend. Most experts consider machine learning as the key to make IoT more intelligent. Arthur Samuel, pioneer of machine learning, defined machine learning as “The field of study that gives computers the ability to learn without explicitly programmed.” The process of machine learning follows the “knowledge hierarchy”—data, information, knowledge and wisdom. Examples of predictive maintenance solutions of IoT are Azure IoT Suite from Microsoft and IBM Watson IoT.

4. Personal Voice-Based Assistants:

Machine learning can also be used to provide customized products and services, including generating music playlist that fits your current mood from your own usage history or trends, or pushing notifications and schedule reminders at a suitable time. Google Assistant, Apple Siri, Amazon Alexa, and Microsoft Cortana are examples of intelligent voice-based assistants with machine learning technology.

5. Business Intelligence:

Business intelligence is a part of data management technology. It organizes and analyzes business information, provides current reports and predictive views for business operations. Companies like Amazon, Google, IBM, and Microsoft have been bridging with their own business platforms with machine learning. For instance, it is easier for Amazon Kinesis Analytics to integrate with Amazon ML, and for Cloud ML by Google to integrate with other Google Cloud products, such as Google Dataflow, Big Query, and etc,.

Five cloud services that have been heavily influenced by machine learning by Forbes Magazine. (retrieve from Janakiram MSV)

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