Auger offers the industry's most accurate AutoML Auger’s patented Bayesian optimization search of ML algorithm/hyperparameter combinations builds the best possible predictive models faster. Auger also provides the most powerful API for AutoML, allowing any developer to build predictive models from their data with no data science background.
Analysts, data scientists and developers can manage the features, target and algorithms attempted for their dataset via Auger’s easy to use model manager. Learn more
Auger provides the most accurate Automated Machine Learning solution within bounded timeframes.
In the example above we ran Auger against other leading AutoML tools using Microsoft’s own 89 datasets chosen to highlight their accuracy. Auger’s error rate on these difficult classification problems was less than 20%, reducing errors from Azure and Google’s AutoML by more than 20%. On more representative datasets the advantage is larger.
The user provides structured data via CSV files or relational databases.
They identify the target and contributing features and Auger determines whether it's a regression, classification or time series experiment.
Auger then preprocesses the data: imputing missing values, removing overly sparse, low variances and over-correlated features, and generating additional features using a variety of heuristics.
Auger searches through thousands of algorithms and hyperparameters to find the best performing models and creates ensembles to further improve accuracy.
The results are presented in a leaderboard in real-time for the user to view and interpret.
The user selects a winning model to deploy. Auger creates a web service prediction endpoint to generate predictions or classifications from new data encountered.
