The differences between conventional Machine Learning and real-time Machine Learning

The former uses batch data processing, while the latter, based on the stream processing model, deals with data that becomes available over time.

Not all predictive models built with Machine Learning are the same. There are those that assume that the behavior of the data is always the same, and therefore degrade over time and lose assertiveness, and those that are automatically updated in real time (on-the-fly) maintain a high level of accuracy over time, regardless of changes in behavior that may occur.

In this article we'll explain the differences between these two types of predictive algorithms and their practical applications.

What Machine Learning does is transform a massive database of data, commonly referred to as Big Data, into predictive models, which are mathematical and statistical models that predict the future. Today, the application of these models is increasingly extensive and these predictive algorithms try to "guess" practically everything: from events - what will the weather be like tomorrow? - to human behavior: what products should I offer my customers to increase sales? 

The conventional models used by most companies are based on a type of data processing called batchwhile the second is based on stream processing. stream processing, i.e. data flow.

The two terms - Batch and Stream - already provide the first clue as to how these two types of predictive models are fueled by data.

In the Batch model, also called traditional Machine Learning, data is collected over a period of time and then this batch of information is sent for processing, analysis and supply to the predictive algorithm. 

In the Stream model, also known as on-the-fly Machine Learning, as soon as the data is available, it can be processed. There's no need to store it for a long time, as the model can process it in real time.

This is the main difference between the two technologies: one can be updated whenever the data is available and the other can't, because it requires recreating the model from scratch. This gives rise to a number of challenges, such as determining when a model needs to be deleted and a new one created, or what data should be used to create this new model. Is it a good idea to use all the historical data, or is it even feasible (is it possible to wait for days or weeks until the new model is ready for use)?

The Data Stream approach has emerged to process large volumes of data that form a virtually endless stream of data quickly and at low computational cost. In addition, these algorithms generate and update models over time, so that the models are always adapted to the reality of the data and its changing behavior.

Batch models are generally used in smaller volumes and do much better than streamers when this volume is low, precisely because the streamer needs a large volume to converge and form a predictive model. Batch processing can be used in large volumes too, but there will probably be a sampling process for them to work with.

The differences between Batch and Stream data processing don't stop there. 

Predictive models based on Batch data processing take longer to implement: the stages of modeling, selecting and pre-processing data can take months, while algorithms powered by Data Stream can be picked up quickly and work with new data whenever it becomes available.

Batch models also have a very short life cycle when applied to dynamic environments because they quickly become obsolete. Stream models, on the other hand, are valid indefinitely because the system automatically detects variations and adapts the model automatically. 

The 4KST predictive model

4KST's technology is unique. With our own algorithm, we develop incremental and adaptive predictive models based on Stream technology.

In other words, we're not stuck with data from the past. And our models are able to update themselves in real time.

As a result, we reduce fraud, chargebacks and evasion, and make the best sales and demand forecasts. We offer you what you need to operate in an increasingly dynamic world.

What's more, our models have low maintenance costs and can be scaled up easily.

 4KST has a range of Artificial Intelligence solutions that can be used in practically all segments of the economy simply, quickly and at low cost. 

Are you interested in our Machine Learning technology? Visit our website and see all our products!