After the disruptive events of 2020, more organisations than ever before have recognised the power of cloud computing. Often cited as a key factor for many businesses’ continued success in the face of the Covid pandemic, cloud computing offers a host of benefits that provide real value to enterprises of all sizes, and across all industries and sectors.

The most commonly mentioned benefits for business include scalability, cost-effectiveness and security, and while these are certainly all compelling reasons for choosing a cloud-based solution, there is far more to the technology. Those enterprises which focus on the machine learning capabilities of cloud computing can enjoy some transformative results.

What is machine learning?

Often shortened to ML, machine learning is the means by which artificial intelligence (AI) is developed. AI then becomes the main driver of the use of ML in the enterprise. The two concepts are inextricably linked, with AI seeking to replicate human thought processes such as decision making, while ML can be deployed as a means to automate almost any task, regardless of whether it normally involves human perception.

Machine learning starts by taking data and analysing it for patterns. This allows predictive capabilities to develop, which will be enhanced as more data is introduced. There are four primary kinds of machine learning:

Supervised machine learning: The most often utilised form of ML, this involves feeding an algorithm large quantities of labelled training data, so that it may learn to make predictions about unfamiliar (unlabelled) data.

Unsupervised machine learning: This involves feeding an algorithm with volumes of unlabelled data, so that the computer may draw its own correlations. It is usually used in advanced AI development, and is commonly used for applications which aim to uncover groups with a data set (clustering), or predicting rules about data (association).

Semi-supervised learning: Similar to supervised learning, but here algorithms use small quantities of labelled data to train with. It’s useful if there is a lack of access to good quality data.

Reinforcement learning: This approach uses a system of punishment and reward to teach the AI the best way of solving a problem, according to given sets of guidelines and instructions.

Real business applications

While these four approaches may seem far removed from everyday business applications, they are in fact the basis of the wide range of diverse technologies popularly used today. Everything from sales forecasting software through to AI powered customer service bots begins with these ML models, and, as a result, ML use by organisations is flourishing. ML allows enterprises to carry out tasks on an unprecedented scale, which not only improves vital efficiency but can also lead to opportunities for business development.

Key functions of ML for businesses

Machine Learning adoption is growing among enterprises, with the following examples of its use already making waves.

Recommendation Engines: A perfect tool for getting the right product in front of the right customer, at the right time, recommendation engines are now used by many e-commerce brands.

Fraud Detection: With the increasing volume of online transactions, this provides a vital service by studying characteristics of authentic transactions so that it can identify any that appear suspect.

Customer Analysis: ML can draw on the vast ‘data lakes’ produced by customer interactions in order to improve and individualise marketing strategies.

Any process that uses or creates sizeable quantities data can be improved with machine learning, bringing your business impressive results. From saving time and money through to enhanced marketing capabilities and the ability to optimise your customers’ experience, there is no limit to the ways that ML can enrich your business. With many more organisations recognising its power, there’s never been a better time to embrace Machine Learning and see your business thrive.