Machine Learning

Integrating Artificial Intelligence

What is Machine Learning?

This simplistic form of artificial intelligence (AI) provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.

AI focuses on the development of computer programs that can access data and use it to learn for themselves. It looks for patterns in data and makes better decisions in the future based on the examples provided.

The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.


At FlowWorks our solutions are based on a hybrid of Supervised Machine Learning and Unsupervised Machine Learning.

Supervised Machine Learning

Training with datasets and domain expertise to provide predictive outcomes based on past events and current data.

In Supervised Machine Learning, algorithms apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values.

The system can provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors to modify the model accordingly.

Unsupervised Machine Learning

Finding previously unknown relations and correlations between data sets and results.

In Unsupervised Machine Learning, algorithms are used when the information used to train is neither classified nor labeled. It studies how systems can infer a function to describe a hidden structure from unlabeled data.

The system does not figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.


Traditional Model

This rigid method provides data without revisions or training.

Cross icon Limited data training.

Cross icon Narrow predictions results.

Cross icon Static cycle increases risk of redundant data.

Machine Learning Model

The FlowWorks model provides consistent results that allow users to compare against historic data for predictive analysis.

Tick icon Uses historical, real-time environmental data and weather forecasts.

Tick icon Compares real-time measurements and predictions.

Tick icon Uses Machine Learning to constantly improve future predictions.