Machine learning (ML) is a kind of artificial intelligence that uses large volumes of data to train algorithms. When these algorithms are applied to fresh data, they can identify and label the content it contains, or make accurate predictions, or unearth patterns.
Surface untapped value from your data with ML
This is a very different approach to conventional IT, where you have to provide precise rules to allow a system to complete a particular task. With ML, the system works out what the rules are on its own, through training, and can improve its performance over time. ML can also deal with a much greater level of uncertainty and variation in the data: in the case of an artificial brain developed by Google, the algorithm learned to pick out cat videos on YouTube even though it hadn't been fed any information about the specific features that can be used to identify cats.
For businesses, ML allows them to surface the untapped value in their data to deliver competitive advantage. Just how important it can be to a company’s success is shown by survey from MIT which found that 60% of leading businesses have already begun implementing an ML strategy. Over a quarter of those businesses are already spending more than 15% of their IT budget on ML.
Machine Learning has applications in every industry
Here are some of the main examples below:
- Retailers are using ML to improve their supply chains, through better forecasting, as well as in systems that recommend related products to customers browsing their websites
- Manufacturers are using machine learning to automatically control the temperature, humidity and other environmental factors in their production facilities — often reducing their utility bills significantly — or deploying ML to identify defective products by automating visual inspection.
- Media and Gaming companies are using ML to automatically classify content to allow it to be easily searched, along with identifying the characteristics of their most profitable customers so they can target them with appropriate offers.
- Financial Services companies are using ML to detect fraud and analyse customer risk when approving credit applications.
Using ML to enhance cybersecurity
ML is also playing a significant role in helping IT teams across all industries to enhance their cybersecurity efforts, by identifying and automatically responding to suspicious patterns in network traffic and server loads. In fact, it’s a key benefit of ML for over 85% of early adopters.
More than 85% of ML workloads run in the cloud
Despite its advantages, however, ML can seem out of reach to many companies. It requires massive and flexible resources, along with deep but rare technical expertise. That’s why companies are looking to the cloud to deliver their ML projects — with more than 85% of ML workloads running in the cloud — and why cloud providers like Google are developing easy-to-use tools that give every company access to these powerful technologies.
Working with our data analytics and AI team
Our Data, Analytics and AI practice brings together a highly committed team of experienced data scientists, mathematicians and engineers. We pride ourselves in collaborating with and empowering client teams to deliver leading-edge data analytics and machine learning solutions on the Google Cloud Platform.
We operate at the edge of modern data warehousing, machine learning and AI, regularly participating in Google Cloud alpha programs to trial new products and features and to future-proof our client solutions.
We have support from an in-house, award winning application development practice to deliver embedded analytics incorporating beautifully designed UIs. We are leaders in geospatial data and one of the first companies globally to achieve the Google Cloud Location-based Services specialisation. If you'd like to find out more about how we can help you build your own modern data and analytics platform, why not take a look at some of our customer success stories or talk to our data analytics team.