The interface of Dataiku DSS was designed to make creating potent machine learning models easy. Clicking through the interface is enough for most use cases, whether you are an expert Data Scientist or a beginner! Discover below what you can do in the visual interface:
The models saved in the flow can be retrained, versioned and monitored. These capabilities are critical to all predictive applications used in production. See here how to handle the whole lifecycle of a model.
Dataiku DSS lets you use multiple machine learning engines within its guided machine learning framework:
|Spark MLlib||H2O||Vertica Advanced Analytics|
Deep learning offers extremely flexible modeling of the relationships between a target and its input features, and is used in a variety of challenging applications, such as image processing, text analysis, and time series, in addition to models for structured data.
The python and the MLlib machine learning engines allow you to define custom models by adding your own code while still taking advantages of the Dataiku DSS visual interface for machine learning.
A saved model can be deployed into a Dataiku DSS API node to query a prediction on new data.
The API node provides all the necessary features for scoring in production:
Dataiku DSS allows users to code everything by themselves in Python, R, Scala, SQL or Shell. See the portal on coding in Dataiku DSS for more information.