this is a Guest Post by Danish

Impact of ML on the Cloud

Today, organizations are becoming data-driven with customers generating the bulk of data every day. With the ability to effectively analyze and generate insights from this data, organizations have able to refine their existing business strategies.

Companies could develop data-driven insights using machine learning techniques. But a lack of the resources or technical know-how is a big constraint to developing machine learning platforms. Cloud is the solution to these issues with major players like Google has begun to offer machine learning tools with the cloud as their platform.

This post discusses how machine learning impacts cloud, which in turn helps businesses, big or small, implement its algorithms to their data and get actionable insights.

Machine Learning and the Cloud

According to Rosenthal, “We are in an era in which machine learning will be important for all businesses, and entrepreneurs should start exploring and investing in it now. Consumers are likely to expect intelligence from your services, and brands that have competencies in data science and machine learning helps develop defensible competitive advantages.”

The cloud has a vital role to play in most business landscapes and ML makes sure that the cloud keeps up to its potential. Moreover, Marty Puranik, founder and CEO of Atlantic.Net, says “By integrating with machine learning, cloud computing becomes better as it can become more interconnected or intelligent with time.”

Cognitive computing. Cognitive computing and Big Data have enhanced the way we communicate and perform business operations online. With the help of data mining, pattern recognition, and natural language processing, new machine learning models try to simulate human thinking. The seamless integration of cloud ensures intelligent computing.

In the future, more programs based on AI engines that deploy face detection, motion detection, visual recognition, video analytics, etc. can proliferate day-to-day operations and improve self-learning systems.

Cloud in the form of public, private, and hybrid systems are part of most organizations and implementing ML to this cloud infrastructure needs one to have knowledge of machine learning tools and cloud computing-based software. If you are a beginner, you can take a machine learning certification course to get down to its basics.

Chatbots and personal assistants

Personal assistants and chatbots are widely used to trigger a conversation or interact with human users. Acknowledging conversational patterns helps machine learning engines improve their learning capabilities and offer a personal, interactive experience to users.

Internet of Things

Being a part of the decade-old information trend, data-based cloud platforms have developed into a comprehensive virtual environment, which integrates all IoT components and takes them to the next level. With a solid cloud infrastructure, companies can connect multitudes of IoT devices to one unified database to make collection of data easy. And add machine learning to the mix, and you will have an intelligent virtual ecosystem that will monitor and self-regulate its operations.

In this scenario, machine learning makes IoT an intelligent system.

Business intelligence

In the absence of cloud, most organizations manually and locally mined data depending on the users’ habits. Cloud computing allows businesses to connect all this data and find underlying patterns. Automation entered the picture when machine learning came into being. Systems that do not need manual input are highly efficient.

Security and data hosting

Cybersecurity has become smarter with the help of machine learning. Complex algorithms streamline data sent from and to servers by effectively evaluating it to detect anomalous patterns and pinpoint intruders. They aim for the total elimination of false alerts and prevent data breaches. Machine learning significantly impacts data hosting. As machine learning empowers load balancing, data centers may experience faster data flow.

AI as a Service

Many cloud service providers offer AIaaS with the help of open source platforms. With this, users get access to different tools to perform necessary AI functions. Deploying AIaaS eliminates the need to contact AI experts as it is a comprehensive delivery model that offers fast and cost-effective solutions to complete any task. Moreover, this makes the process of intelligent automation faster because it removes complexities associated with the process.  This results in a sharp increase in the use of the cloud capabilities.

Wrapping up

Cloud and ML make a symbiotic relationship!

The future is an intelligent cloud. Now, cloud computing and artificial intelligence are interdependent on each other, and that might result in the development of advanced applications.

Different sectors like banking, education, and hospitality will take advantage of an intelligent cloud, increasing the efficiency and accuracy of the services they deliver. For example, a virtual assistant in hospitals can lower doctors’ customary load of decision making by analyzing and evaluating cases, comparing with past cases, and promoting new approaches to diagnosis.

A growth of machine learning makes cloud computing easier to handle, faster to deploy, effective to scale. The more the implementation of cloud in businesses, the higher will be the use of machine learning. The future is near when machine learning will change the way organizations deploy, implement, and use the cloud.

Credits: Danish Wadhwa (Digital Strategist), Founder @ Fly.Biz