Machine learning development
Machine learning (ML) is a class of AI techniques that focuses on building systems that learn – or improve performance – through data analysis. Its world-class fintech and cloud engineering team with a solid background of practice that combines consulting, strategy, design and engineering at scale, can help with outsource machine learning development and provide advisory services.
Machine learning applications
Machine learning business goals
Modeling the value of the customer service life cycle
Modeling the value of the customer service lifecycle is very important not only for online sales companies, but also for businesses in other industries. Customer service cycle value models are especially effective at predicting the future profit that an individual customer will generate over a given period in the future. This insight helps you focus your marketing efforts on the customers who bring you the most value, encouraging them to engage with the brand more often. Customer service lifecycle value models also improve targeting efficiency and thus attract new paying customers.
Image classification capabilities
Beyond retail, financial services, and online sales, machine learning can be used in a wide variety of scenarios. It is used very effectively in the scientific, energy and construction industries, as well as in healthcare. Deep learning techniques such as neural networks are often used to classify images. They are good at identifying the most important characteristics of the image, even in the presence of secondary factors. For example, neural networks distinguish between perspective, lighting level, scale or interference and can adjust image characteristics to provide the best possible result.
Customer churn prediction model
Attracting new customers requires more financial and time costs than maintaining the level of satisfaction and loyalty of existing ones. Simulation helps identify customers who might leave and why they leave. An effective model uses machine learning algorithms to assess and rank everything from risk metrics for individual customers to causes of churn. The results obtained play an important role in the development of a retention strategy.
Machine learning is present in all spheres of life today. Every time we use banking services, shop online or communicate on social media, machine learning algorithms help make this interaction more convenient, efficient and secure. Machine learning and related technologies are evolving rapidly. Their capabilities today are just the tip of the iceberg.
Machine Learning: Custom Development
To avoid mistakes when developing custom machine learning , it is helpful to outline the steps involved in this work.
Since the duration and complexity of the development of machine learning systems, depending on the requirements, may differ by several orders of magnitude, the first step is to fix the desired result that the customer wants to get.
Having finished with the formulation of the problem and having roughly decided on the solution (and also making sure that the problem can be solved within a reasonable time frame and budgets), we proceed to the next step: collecting data.
Next, comes the most crucial stage – data markup. The labeled data is divided into several groups: the training group is transferred to the machine learning engineers, and the test data remains with the client. This insures the customer against the most common mistake of machine learning – the so-called overfitting: artificial intelligence can learn to solve a problem with the highest accuracy on a training set, but be absolutely helpless on unfamiliar data.
The learning process itself is the subject of multi-page scientific articles and a whole direction in science, therefore, omitting the details, it is worth noting that ML engineers select learning methods from simple to complex, controlling the quality of models using several selected metrics.
To minimize ML development costs as much as possible, we always first look for previously created ML solutions that can be adapted without a full training cycle. Trained models are necessarily tested on data that was not involved in the training process. This stage can be performed automatically, showing the customer the progress in development almost daily.
Since machine learning methods are very demanding on hardware resources, at the final stage, optimization of the ready-made solution for memory or processor consumption is often required. Sometimes it is enough to sacrifice a fraction of a percent of the accuracy of the algorithms to speed up the ML solution dozens of times.