Flek makes data science and AI Analytics easier by bridging the gap between data and insight. It carries out this by:

• Helping organizations leverage their existing expertise (people), data (SQL databases) and workflows (pipelines).

• Simplifying machine learning, model building and model maintenance since all modeling activities are based on standardized probabilistic techniques.

• Allowing models to be shared for both exploratory and predictive analytics across the organization and for a variety of AI citizens. 

• Integrating in one Python Toolkit: data preparation, model building, querying, mining, auto-discovery , visual exploration, prediction and recommendation.

• Enabling data science citizens to interpret and explain the results of their analytical activities - be it during exploration, discovery or prediction.

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Examples

Let's suppose in a large organization an analysts wants to explore customers' profiles; while the loan manager wants to categories new loan applicants based on their riskiness; and maybe the product manager wants to predict which customers might be interested in a new line of credit.

 

​Flek makes it simple to conduct these diverse tasks in a standardize and holistic approach. The analysts can dive into details of customer profiles to uncover probabilistic patterns or spot anomalies. While the loan manager can get more insight by running a classification task using the probabilistic model (running on the engine) to segment loan applications. Whereas the product manager can run a prediction activity that takes existing customers as input and then predicts the level of interest in the new line of credit (given their credit history and profiles); a kind of simulated recommendation use case.​