Machine learning (ML) offers the remarkable potential to transform business operations. At Neural Technologies, we have over 20 years’ experience actively deploying ML technologies for customers across a wide range of industries.
The question of how to choose a machine learning algorithm is a complex one for technical teams approaching these solutions for the first time. Choosing the right solution means understanding the key features which you should look out for.
While many technology providers discuss the power of machine learning, Neural Technologies is a genuine pioneer in applying these solutions. That gives us a unique insight into how to choose a machine learning model that fits your needs. Here are some key considerations to take into account.
Focus on data
The focus of a machine learning solution should always be on data. There’s no point transforming for the sake of transformation. Implementing a machine learning platform should be done with a clear understanding of capturing data to turn into actionable insights.
Consider major technology firms such as Google and Facebook. They have carefully engineered their products to capture data, recognizing the power of what can be achieved if this is implemented appropriately.
We see this same value realized for customers of Neural Technologies, helping reduce revenue leakage, optimize operations and business decision making, reducing fraud, and generating valuable insights that can unlock new market opportunities.
Ensure agnostic data analysis
An effective machine learning algorithm must be agnostic, and able to look beyond familiar data patterns to capture the ‘unknown unknown’ of unique events. That should go beyond a simple, static rules-based approach to a more dynamic and adaptive solution.
This means an effective model should not only recognise familiar data patterns, but also be able to structure the data itself and provide anomaly detection in areas such as emerging fraud patterns. True machine learning doesn’t just learn, it evolves.
Automated but understandable
There is currently a notable shortfall of expert talent and resources in machine learning, which means valuable tools must be both automated, and explainable. It’s critical that you adopt a solution which allows you to deal with high throughput data but delivers the results in a way that allows real-time or near-real-time business intelligence.
An effective solution must be user-friendly and accessible, so that the benefit of machine learning isn’t simply limited to one or two expert analysts or machine learning specialists. It also means you’re not exposed to significant challenges if your key machine learning talent leaves the organization. That’s a particularly important element given the huge demand for machine learning experts and technical leads in the current business landscape.
Real-time monitoring
A machine learning model should provide actionable insights in a timely manner. That means the ability to collect, collate, and present data in a way that addresses the required 6 Vs of big data.
But the importance of ‘real-time’ in a true machine learning solution should also include active and ongoing analysis of the machine learning algorithm itself. Real-time streaming data analytics offers huge advantages, but you must be able to trust the data. Ensure appropriate processes are in place to verify processes and systems during ongoing operations.
The system framework
There are now a wealth of open source libraries in existence providing access to fundamental machine learning algorithms, but the right framework is needed to bring these aspects together for business production.
We also now have more powerful computing and processing technologies that offer the foundation for effective machine learning solutions. The processing needs of a machine learning solution should not be overlooked when it comes to adopting a fit-for-purpose technology for a given organization. Ensure your fundamental IT stack is suitable for the implementation, or that an appropriate cloud-based solution is available that places the burden of processing power elsewhere. You’ll still need appropriate IT infrastructure to access the cloud-based solution of course!
A partner with experience
Neural Technologies is a pioneer in machine learning. While other companies are still tentatively discussing ML ambitions, we’re building on over 20 years of real-world experience for business tackling fraud and reducing revenue losses in telecoms and beyond.
Our flexible Optimus machine learning platform is designed to provide a dynamic, adaptable solution to fit your own unique business needs. That means a powerful and tested technology that adapts to optimize outcomes for your business.
We work with customers to deliver fit-for-purpose solutions adapted to their specific operating environment, to deliver solutions that deliver real business value.