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Machine Learning



Guided Machine Learning

Getting started

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:

Batch scoring
  • Learn how to save a model to the flow and use it to score another dataset.
  • A Dataiku model consists of a whole pipeline that combines data preparationfeature handling and a ML model. This means that you can directly score raw data with a Dataiku model, without reimplementing data cleaning nor feature preprocessing.
The lifecycle of a model in production

The models saved in the flow can be retrainedversioned and monitored. These capabilities are critical to all predictive applications used in production. See here how to handle the whole lifecycle of a model.

Machine learning engines

Dataiku DSS lets you use multiple machine learning engines within its guided machine learning framework.

Deep Learning

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.

Custom models

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.

Score in real time through a REST API

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:

Prediction Examples

Revenue forecast
Energy & environment

Clustering Examples


Full Control With Code

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.