Overcoming the challenges of
limited data to make Machine
Learning personal

When you hear about artificial intelligence (AI) and Machine Learning (ML), images of advanced robotics and Hollywood films might be the first things that rush to your mind.

However, these technologies aren’t designed to replace the human workforce but to enhance the overall working experience.

In recent years, ML has crept into industries worldwide, helping businesses boost their digital transformations and tap into otherwise inaccessible benefits. But the technology has only just started gaining traction within the transport sector.

Designed to tell you what you don’t know, and help you predict the future, ML, as the name suggests, learns complex patterns from massive amounts of data that the human mind or older systems are unable to process. However, what if there is a lack of data?

With ML, CloudMade’s primary goal is to develop personalized learning solutions that understand and adapt to a driver’s individual behavior. For this to become a reality, model training focused on individual data is a must. For example, predictive navigation entirely depends on personal driver behavior.

So, when there is a lack of personal data in machine learning, it fails to achieve a reliable project.

Limited training data: the problems

A low amount of training data to train sophisticated models poses a serious challenge. When deep learning models are unsure about the result, rather than showing uncertainty, it completes the operation without any red flags. This is troublesome because not only does it fail to achieve high accuracy, but lack of data also means more hidden doubts.

Additionally, collecting individual data takes time. It takes even more time to train models using this data in order to make them capable of adapting fast to the behavioral changes.

Classical ML approaches perform well when they are training on big datasets. When the data volume is limited, they usually generate poor results.

So, is there a solution?

Collecting data by yourself or relying on open sources won’t help you gain sufficient personal data. Here are some measures to tackle the main ML obstacle.

  • Leverage data from various sources

The electrification of the fleet has introduced a lot of tech applications being used onboard vehicles, which creates a lot of data. For instance, GPS data helps vehicle fleet managers track vehicles, and applications that track fuel consumption notify of a problem if the fuel consumed in a particular vehicle increases suddenly.

Most data scientists use data from various sources, such as telematics data, mobile phone data, etc., to gain insights into behavior patterns and build learning models.

  • Use different approaches

Consider using approaches that are less sensitive to data volume, like probabilistic models, regression, etc. Such models perform relatively well on small data sets. For instance, the probabilistic model learns from small data sets and offers the load profile and EV charging pattern for 24 hours.

  • Group-based and personalized models

When there is limited personal data to train ML models, consider using ensembles of group-based models and personalized models. For example, driver style analysis where data from different users can be used to define main features that help improve the accuracy of the model significantly.

At CloudMade, we are working on an ML model for a group of drivers with “similar” behavior that can be applied to a current driver as a basic approach that will significantly speed up the learning process. Furthermore, in the process of obtaining new personalized data, this model is capable of adapting to the individual characteristics of each driver.

  • Focus on feature engineering

Feature engineering is a technique used in ML that uses available data to come up with new variables that aren’t already in the training set. The models can be applied to a group of users to define similar features (both supervised and unsupervised learning) that speed up data transformations and enhance model accuracy.

  • Continuous re-training and validation

To adapt quickly to sudden behavioral changes, it is essential that you continuously keep re-training and validating the models on every updated dataset.

At CloudMade, we have leveraged this approach to develop an engine that constantly tracks the performance of the model and adapts the solution to new behavioral patterns.

The bottom line

The lack of training data and longer learning times are two significant challenges in developing ML models for personalized learning. Ensuring all available data points are used with maximum efficiency demands much more effort in data pre-processing and feature engineering. However, using the data from different sources and other users (where it is applicable), and building pipelines that use the advantages of groups and individual approaches provide excellent solutions for those problems.

Interested in learning more about the benefits which CloudMade can bring to your business?

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