One thing is common among the cloud juggernauts Amazon, Google, and Microsoft: All three embrace the machine learning technology to make their cloud services more competitive than one another. Machine learning is not a new term for cloud computing, but its use keeps on increasing with time for good reason.
Read on to know how machine learning concept is going to shape up the future of the cloud.
The search engine giant Google sees machine learning along with deep analytics as the future of the cloud, and therefore, it focuses on machine learning and betting big on implementing machine learning for faster Big Data prediction and improved search engine algorithms. It has released a few machine learning services for speech recognition and image recognition. Similarly, Amazon Web Services (AWS) and Microsoft have also launched ML (Machine Learning) platforms that enable predictive analytics applications to operate with ease.
Here are some of the noteworthy reasons for the inclination of renowned SaaS providers toward machine learning while developing user-friendly cloud platforms:
A few years back, machine learning systems were very costly and too complex to handle for the enterprises. Today, the cloud technology has changed this scenario while making machine learning more practical and applicable than ever. ML platform is must-have for predictive analytics. ML algorithms play a vital role in leveraging big data for stopping fraudulent transactions, reducing customer churn, and making product recommendations.
Scalability and flexibility
Machine learning systems are highly scalable and flexible when it comes to data storage and access. Interestingly, the cloud platform becomes cheaper as we use it for storing more and more data. The same is true for the machine learning systems. An ML platform’s super-scalability makes it useful for the big and med-sized enterprises while enabling them to cope with challenging market conditions and intense competition.
These days, the ever-changing business requirements contribute in a massive upsurge of data. Large databases have complex statistics, and they create problems in analyzing. ML systems can facilitate multiple data analysis and makes stats available in a simple and easy-to- understand form.
The cloud is a perfect place to integrate machine learning workloads because both facilitate variation during data storage and access. Cloud-based IaaS and SaaS providers can quickly switch between two different aspects of the process: Training and running. It is possible because of high variation in machine learning systems. It facilitates entrepreneurs to use the storage required for their business operations while saving them from excessive storage space and extra costs.
We require to store and retrieve zillions of data in the future, and the machine learning will make our job easy. It makes the cloud more flexible and scalable, while facilitating data access and analysis. There is no exaggeration in considering it the future of cloud computing.