Our introduction to Machine Learning (click here) Blog
Machine learning is growing in reputation every day, and it is steadily making its way into enterprise application areas like fraud detection, business intelligence, and customer support. Machine learning is related to computational learning and pattern recognition, and the number of public cloud service providers have now made the technology that was out of reach to most enterprises affordable & available today.
Cloud computing is also a technology that has become an integral part of companies today due to its importance to the digital world. Nowadays, companies don’t have to work with bulky hardware, which has resulted in a considerable drop in the general cost of operation. According to statista, the cloud services market will increase at a rate of 23.3 percent between 2016 and 2020. RightScale carried out a survey in 2018 in which 96 percent of the respondents use the cloud.
Using cloud computing alongside machine learning will increase the need for intelligent clouds, which will be the most disruptive change in the technological market. According to an article from IBM, fusing cloud computing and artificial intelligence is the next big step in the market.
Limits of machine learning without the cloud
The basic idea behind using machine learning systems is predictive analytics. The system will be able to make predictions based on data gotten from past events.
At its early stage, machine learning was not used alongside the cloud, so companies that wanted to utilize these systems faced some problems.
Some of these problems include:
The high cost of implementation
Machine learning requires a considerable amount of data be analyzed to adequately train the system. The problem though was that to cater for the massive volume of data needed; these companies needed a tremendous amount of storage and processing power. Therefore, businesses interested in using these learning systems had to shell out vast amounts of money for the necessary software and hardware.
Difficult to implement and manage
Without the benefit of storing the massive amount of data needed for the machine learning systems on the cloud, organizations have to install bulky hardware to handle this data. Because of the cumbersome nature of the hardware, the installation process was quite tedious. Also, managing the system would require more personnel, which further adds to the cost. This means that your workforce that would have otherwise focused on other productive things would have to concentrate on managing the data for your machine learning systems.
The current state of machine learning on the cloud
The combination of machine learning and cloud computing has brought significant changes to the world of IT and many other industries. Machine learning systems build models from transactional data and then makes predictions using patterns found in the data. Some services influenced by the union of machine learning and cloud computing include.
Cognitive computing involves bringing sensory capabilities like listening, talking, and sight to apps. Cognitive computing empowers developers with APIs based on face detection, video analytics, language translation, and so on.
IoT is a data-driven platform that has been around for a while. It involves acquiring data from different sensors to process and analyze them. Machine learning has made IoT more intelligent by working in tandem with IoT to develop the right model that will best understand the patterns of the generated datasets.
Machine learning has made voice-based personal assistants into the current business trend. These assistants help you in making decisions by learning from your usage trends and past choices.
By integrating machine learning into the decision-making process of your business, you would be able to make better and more accurate forecasts. The system will offer you intelligent insights derived from existing data.
The use of bots is now a growing trend in business today. Companies are now relying on bots to drive customer support and service. Although bots have been in existence for a long time now, the introduction of machine learning has made them very useful today.
Why machine learning needs the cloud
Machine learning and cloud computing are two great innovations that have revolutionized the IT world today. They have both found use in almost every industry, and their importance keeps I growing. AI and machine learning have seen incredible growth over the years with tech giants like Google and Baidu spending between $20 billion and $30 billion in 2016 alone. Cloud computing has also grown exponentially in the last few years, and it will continue to grow. An article on Forbes indicates that about 83 percent of enterprise workloads will be shifted to the cloud by the year 2020.
One of the most significant issues of machine learning is the rapidly increasing volume of data that is used for training the system, so there is a need for an efficient way to handle the data. Fortunately, one of the most significant selling points of the cloud is the fact that the more the data put into it, the cheaper it gets for all its customers. Therefore, with cloud services, machine learning will continue to thrive.
Using cloud services with machine learning saves companies the cost of acquiring the bulky hardware required to handle significant data. So, you can focus your capital and human resources on other aspects of your business.
The growth of machine learning and cloud computing is quite inevitable. It is left to you to join the trend and take advantage of these technologies. Machine learning helps organizations make an accurate forecast, and with the help of the cloud, more data can be analyzed, thereby increasing the accuracy of the estimates. Apart from this, using the cloud is much cheaper than privately managing the amount of data needed by the machine learning system. Therefore, if you are still on the sidelines regarding this trend, you need to get into the game as soon as possible.