The cloud as we know it is changing. Secure remote data storage is still the main function of the cloud. We’ve also mentioned the role of cloud servers in data analysis, particularly when we look at the vast amounts of data generated by the IoT. The next phase of development brings this powerful analysis to a new level with the introduction of machine learning. The technology used to be limited to large companies who could afford the time and cost of purchasing proprietary programs or having bespoke software designed for them. With global servers like Microsoft, Amazon and Google powering the market, machine learning is now within reach for any small business who is keen to experiment with the technology. This powerful combination is beginning to be known as the ‘intelligent cloud’.
The scope for machine learning is very broad. It can be implemented in various capacities across inventory management, machine monitoring (for IoT devices), predictive marketing, fraud detection and cyber security. One of the first ways people will interact with machine learning is through chat bots and virtual voice assistants. Through natural language processing and retaining data from each interaction, chat bots are serving customers online while saving staff costs for companies. Machine learning outcomes are diverse enough to be both client-facing and back-end focussed. This article will introduce how machine learning works, the most common types of analysis it can make and how the information can be relevant to businesses.
Machine learning in the cloud
Machine learning requires a few key things to be effective. The first is data. The more data machine learning program can have the more accurate it will be. Of course, the beauty of this process is that each time it makes a decision it adds the outcome to the data set – essentially getting ‘smarter’. The ability to process this data is predicated on the support of powerful storage capabilities. This is where the cloud steps in. The cloud offers storage solutions for the data sets and powers the programs, so they can make adequate and timely use of them. To provide some context, a cloud-based machine learning program can create thousands of accurate models in moments, whereas a competent analyst may only be able to produce one or two over a week (depending on the complexity of course).The key that unlocks the potential of the data and the processing power is the algorithm. Algorithms are directed to harvest and analyse the data then create useful outputs. The outputs become more accurate and useful over time as the results of the outputs are also fed back into the analysis stream as new data.
How can machine learning in the cloud benefit businesses?
There are many different ways machine learning can be accessed and commanded. As the software becomes more accessible businesses are developing new use cases. Here are some examples of how machine learning can work, and how those outcomes can be helpful to businesses.
Binary prediction: The data is analysed and produces a yes/no answer. This can often be done in real time, so the results of the process can be seen as recommended products, ‘what to watch next’ suggestions on video platforms and upsell options on ecommerce websites.
Category prediction: The data is analysed and assigned to predetermined categories. When the data has been sorted preliminarily using category prediction, the selected data can be worked with faster and more accurately. The categorisation process is informed by previously determined data sets. For example, a winery may be able to categorise a particular vintage using data that includes weather patterns, soil acidity results, rainfall and harvest conditions. The insurance industry is another big beneficiary of categorisation prediction thanks to the number of variables that are involved in insurance claims. This type of predictive categorisation is also seen in the credit scoring, retail, manufacturing and finance areas.
Value prediction: The data is analysed to predict outcomes. This is the most complex type of calculation because it involves using multiple data sets to identify patterns from previous activity. For example, a manufacturer may want to know how many units to produce in the next quarter. Targets need to be set to meet demand and also to manage staffing, raw materials and production costs. Previous data that reflects this information for the same time period from the last 2-5 years will be used to make value predictions. Management at almost any level can find benefit from using value prediction models, and results should be easily accessible to decisions can be made promptly.
Supervised learning: When data is labelled correctly, the machine learning processes can run the data against an ‘answer sheet’. Errors can be detected quickly and easily. This type of learning is often used to predict financial fraud or specific customer or claimant profiles.
Unsupervised learning: This is more exploratory in nature than supervised learning. The data is not labelled at all and the machine learning program is tasking with identifying patterns and behaviours. This is deployed in many verticals including market segmentation uses and mapping.
The many styles of machine learning reveal infinite opportunities for businesses to harness the power of the intelligent cloud. The incredible processing power of the cloud makes this technology more available and user-friendly than ever before. Businesses of any kind should be able to take advantage of this new way of analysing data. The information a business can glean has the potential to reduce costs in all areas of the budget from utilities to manufacturing. The ability to reduce fraud, categorise claims and applications and streamline decision-making can make businesses extraordinarily efficient.
About Prosyn, your cloud-based machine learning partner
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