In the 350+ years history of probability, no one has ever attempted to build a Probability Machine, so we set on building the 1st one ever.

Because probability is ubiquitous and foundational to many statistical and machine learning tasks, we believe that the Flek Machine will be a landmark in the AI field.

Flek is a unified framework for AI Analytics. It is a foundational development library that includes 3 main components: FlekML, Flek Server and Python Toolkit.

Essentially, Flek allows data practitionners to build probabilistic models, develop ML-driven applications as well as run both exploratory and predictive analytics – all in one integrated platform.

A Probability Machine is a special kind of machine learning engine that learns, stores and serves Nuggets (probability like objects). It learns these Nuggets from semi-structured data.

It then allows users to query and mine the Nugget store to search for probabilistic patterns or to perform complex probabilistic computations. The probability machine can also serve these Nuggets and make them available for prediction or classification purposes.

For organizations, Flek is geared towards:

• SME (small to medium enterprises) that cannot afford large data science teams.

• Larger enterprises that need a fully integrated platform to help them answer varied AI analytics and data science questions – without drowning in a swamp of complex models and pipelines that are very difficult to maintain and share among different users and use cases.​

For end-users, Flek is intended to serve the needs of a mix of AI citizens: data scientists, programmers, statisticians and business analysts.

Thanks to its varied capabilities, Flek can meet the demands of a wide range of core sectors and business applications that deal with uncertainty and require both exploratory and predictive capabilities.

Explore sectors here.

Both Flek and Relational database management systems (RDBMS) have a core engine inside that serves user requests.

In the case of RDBMS, users can send SQL queries to retrieve a single record or multiple records joined from one or more tables.

In Flek, users can fetch a single Nugget (probability like object) or search the model store using a filter. They can also run auto-discovery algorithms that search for patterns, associations, rules, anomalies or causal relationships.