BigML Releases

MAR 21 2017

Winter 2017

BigML launches Boosted Trees, the third ensemble-based strategy that helps you easily solve classification and regression problems. This Machine Learning technique allows each tree model to concentrate on the wrong predictions of the previously grown tree to correct and improve on any mistakes made in those previous iterations.

NOV 29 2016

Fall 2016

Topic Models is the resource that helps you easily find thematically related terms in your text data. Discover BigML’s implementation of the underlying Latent Dirichlet Allocation (LDA) technique, one of the most popular probabilistic methods for topic modeling tasks.

SEP 28 2016

Summer 2016

BigML brings to your Dashboard Logistic Regression, one of the most popular methods used to solve classification problems. Now you can build a Logistic Regression with a single click, introspect it by using intuitive visualizations, evaluate it like any other classification model, fine tune it via handy configuration options, and create individual or batch predictions from it with ease.

MAY 19 2016

Spring 2016

WhizzML is a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuming analytics tasks.

DEC 15 2015

Fall 2015

BigML is the first Machine Learning service offering Association Discovery on the cloud. With Association Discovery you can pinpoint hidden relations between values of your variables in high-dimensional datasets with just one click. It is very useful for market basket analysis, web usage patterns, intrusion detection, fraud detection, or bioinformatics.

FEB 11 2015

Winter 2015

The enhanced version of BigML includes: the Sample Service for fast access to datasets that are kept in an in-memory cache, very convenient for filtering and correlation techniques; the Dynamic Scatterplot, a graph that lets you visualize your samples differently and it is extremely useful to detect interesting patterns in your data, correlations among your fields, or anomalous data points amidst other observations; the G-Means algorithm, ideal to create clusters when you may not know how many clusters you wish to build from your dataset; the Projects to help you organize your Machine Learning resources created in BigML; and more integrations you do not want to miss.