Liquid ML is a pioneering framework to interconnect enterprise analytics with collaborative data science and machine learning.
Based on the Cloud-Assisted Meta programming (CAMP) paradigm, the framework allows the usage of Currently Best Fitting (CBF) algorithms. Before code interpretation / compilation the concrete algorithms, that implement the CBF specifications, are automatically chosen from local and public catalog servers, that host and deploy the concrete algorithms. Thereby the specification is constituted by a unique algorithm category, a data domain and a metric, which substantiates the meaning of Best Fitting within the respective algorithm- and data context. An example is the average prediction accuracy within a fixed set of gold standard samples of the data domain (e.g. latin handwriting samples, spoken word samples, TCGA gene expression data, etc.).
The Liquid ML framework allows the implementation of cutting edge enterprise analytical applications, that are automatically kept up-to-date and therefore minimize their maintenance costs. Also the Liquid ML framework facilitates the publication, application, sharing and comparison of algorithms, within and between workgroups.