5 ways machine learning is impacting cloud computing

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Our world is changing at dizzying speed. With self-driving cars entering our highways and computers predicting our next likely purchase, we are entering an era where the cloud and machine learning are directly impacting our everyday lives. By merging with machine learning, cloud computing is in the midst of a pivot towards becoming more interconnected and intelligent.

Below are five ways in which machine learning has been shaping the modern cloud.

Cognitive computing

Cognitive computing is rapidly evolving the landscape of how we communicate and do business online. Using data mining, natural language processing and pattern recognition, the models developed aim to simulate human thinking. The promise is for intelligent computing that’s seamlessly integrated.

With the proliferation of APIs, it is becoming easier for developers to access cognitive computing tools to build cutting edge software in the cloud. In the coming years, we should expect more programs powered by AI engines that employ visual recognition, face detection, emotion detection, video analytics, etc. to showcase self-learning systems.

Chatbots and personal assistants

Personal assistants and chatbots belong to a breed of technology designed to simulate a conversation or interaction with human users. By learning from past choices and conversational patterns, machine learning engines can make these bots more powerful in their ability to offer a personal interactive experience. And the demand for it is growing.

When interaction is supplemented with cloud-based machine learning, personal assistants can go beyond addressing simple queries to anticipating users’ needs. For instance, based on time of day, they can create a music playlist to suit your mood, or send you notifications based on where you are and what you are doing. Amazon Alexa, Google Assistant and Microsoft Cortana are commercially available examples of personal assistants.

Machine learning is also flooding the online customer service and support market. In fact, intelligent automation will manage 85 percent of businesses’ customer relationships by 2020, says Gartner. It is attractive to businesses that are short on staff but want to offer a satisfying customer experience. Now, online messaging platforms like Facebook Messenger, Slack or WeChat are using machine learning to make their chatbots smarter, with expanded utility looming on the horizon.

Internet of Things (IoT)

Acting as the unifier for the now two decade old trend, data-driven cloud platforms have created a seamless virtual environment in which all the components of IoT can come together and advance to the next level. But it is machine learning that will be responsible for making IoT intelligent.

IoT systems capture, monitor and manage massive amounts of data from various channels and sensors for further processing and analysis. In order to excel at making multiple algorithms work in tandem, they need the computational power of machine learning.

Such models are necessary to quickly understand and act on the patterns behind datasets generated by interconnected devices. In the case of Industrial IoT, it gives them the power to pinpoint system anomalies to predict and prevent crashes and equipment breakdowns.

Business intelligence

Before the cloud, most enterprise systems manually and locally collected data related to the habits of their users. Cloud computing enabled business to connect all this data together and find underlying patterns. With the introduction of machine learning, automation entered the picture. Such systems, which no longer require manual input, are extremely efficient.

Think of this computational intelligence as a cognitive process built to learn from data to anticipate what will happen next and complete tasks in the background. For instance, ML-powered business intelligence can learn from data users input repeatedly, such as contact forms or online carts, and fill in the data for them.

The entry of machine learning into the arena of business intelligence saves decision makers time. By surfacing complex and detailed patterns, machine learning grants them access to intelligent operational insights and trend forecasts they couldn’t previously see.

Security and data hosting

Cybersecurity has been using machine learning to become smarter. Complex algorithms analyze data flows sent from and to servers every millisecond looking for anomalous patterns to pinpoint intruders. Their goal: Eliminate false alerts and prevent attacks before they happen.

Machine learning will also increasingly affect data hosting. With machine learning powered load balancing, data centers will be able to support better and faster data flow. For instance, live streaming will no longer require buffering as the system will know which platform will need an extra boost, and when.

In essence, machine learning has the ability to bolster the entire IT infrastructure. It can fill in the gaps of staff shortages and make the systems more responsive and efficient.

The connected future

Many possibilities exist when we think of combining machine learning with the cloud. It seems that its evolution will be predicated upon inter-connectivity. As new ML-powered devices plug into the cloud nervous system to share information with other devices and platforms, they will learn even faster, becoming more useful to humans.

Our devices will speak to us directly with a human voice, allowing for greater productivity and faster communication. Repetitive business processes will become automated and people’s brain power freed up to do more creative and strategic work.

This can very well lend itself to your self-driving vehicle anticipating a doctor’s visit and having a parking space reserved for you. It will know which grocery store to take you to after, to pick up an order based on the last recipe you favored on the web. The possibilities are endless, really.

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