Flek makes data science easier by providing integrated toolset that bridges the gap between data and insight
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.
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.