By integrating exploration and prediction, the Flek Machine makes it easier to run complex AI Analytics activities.
AI Analytics within an enterprise can be generally categorized into two kinds: exploratory or predictive. With a clear focus on helping organizations go from data to insight, GoFlek offers its unified platform for AI Analytics.
Beginning, AI citizens upload their semi-structured data and then automatically build their probabilistic model using the core engine. Following, they start interacting with the engine to explore their model: i.e. querying, mining or running auto-discovery algorithms that search for interesting associations, rules, relations, anomalies or causal relationships. Using the same model, and without any training or tuning, they can also run complex predictive analytics tasks.
Thanks to the probabilistic approach and available Toolkit components, users can understand the outcome of their exploration activities by plotting and visualization. They can also trace and validate their predictions to further investigate how they were made.
By leveraging existing data found in SQL databases and making it available for machine learning (with little transformation) the results of analytics are not obscured or difficult to interpret. Overall, there are no black boxes in the Flek Machine; all AI insights - whether they come from exploration or prediction - are explainable and interpretable.