DataRobot
DataRobot

DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods.

«DataRobot is technology neutral, but we want to make the solution available to our Qlik customers for productive use as a first step. They will get an easy introduction to AI, but the visualization will take place in their familiar Qlik environment.» - Rino Mentil (CEO & Founder Informatec)

 

Models and predictions via the intuitive user interface

DataRobot UI

DataRobot offers an easy-to-use visual interface that works for business users and experienced data scientists.  Simply select the target variable and click the start button to get the relevant data models.

 

DataRobot Key-Features

  • Human-centric design, ideal for business users

    Domain expertise is the key to developing effective predictive models. DataRobot enables business users to generate accurate models quickly and perform sophisticated data science functions directly.

  • Built-in guardrails

    With DataRobot, modeling projects follow a consistent methodology based on best practices so users can’t “forget” to perform a critical step, such as model validation.

  • Beschleunigt die Modellauswertung

    DataRobot erstellt eine Rangliste, auf der Sie sehen können, welche Modelle mit Ihren Daten am besten funktionieren und bietet Ihnen die Werkzeuge, die Sie benötigen, um die einzelnen Datenmodelle zu vergleichen und zu erkunden. 

  • Speeds model evaluation

    DataRobot builds a leaderboard so you can see which models perform best with your data, and provides the tools you need to explore and compare individual models.

  • Unsupervised anomaly detection

    Uncover anomalies in a dataset with DataRobot's unsupervised ensemble blend model, which can offer new insights even in familiar datasets.

  • Builds the workflow for you

    DataRobot creates the predictive modeling workflow for you. It knows what to do at each step of the process, and does it automatically, without prior programming or manual input from users.

  • Automates feature engineering

    DataRobot prepares data automatically, performing operations like one-hot encoding, missing imputation, text mining, and standardization to transform features for optimal results.

  • Leverages innovative open source engines

    To harness the most advanced techniques, DataRobot uses open source machine learning libraries like R, scikit-learn, TensorFlow, Vowpal Wabbit, Spark ML, and XGBoost.

  • Supports advanced tuning

    DataRobot automates model tuning, but also supports manual tuning so data scientists can tune and adjust machine learning algorithms for even better results.

  • Multiclass model support

    DataRobot allows for classification on targets with up to 10 distinct values, offering both real-time and batch support for uncovering the predictive class and showing its probability across all classes.

What is Automated Machine Learning?

Automated machine learning is a technology invented by DataRobot to automate many of the tasks needed to develop artificial intelligence (AI) and machine learning applications. Incorporating the knowledge and expertise of some of the world’s top data scientists, DataRobot enables more users across an enterprise to succeed with machine learning by simply utilizing their understanding of their data and business and letting DataRobot do the rest.

The Enterprise Artificial Intelligence Solution

Operating at enterprise scale requires blazing performance, strict adherence to controls, and relentless focus on data protection. DataRobot is an enterprise-ready platform, delivering the governance, training, and world-class support your organization needs to quickly get up and running.

Enterprise Features:

  • Use on-premise or in the cloud

    On-premise: You can deploy DataRobot onpremise on standalone servers, an existing Hadoop infrastructure, or in a Virtual Private Cloud (VPC).

    Cloud: The DataRobot Cloud is hosted on Amazon Web Services (AWS), delivering the flexibility and speed necessary for any enterprise.

  • Leverages distributed processing

    DataRobot leverages modern distributed processing, running experiments in parallel to radically reduce the time it takes to run a complete data science project.

  • Enables rapid collaboration

    With DataRobot, business users, data scientists, and stakeholders work together on machine learning projects to deliver better results with less wasted effort.

  • Eliminates model deployment bottlenecks

    There are multiple options for deploying your finished models with DataRobot, including native scoring, exportable prediction code, and prediction APIs for real-time and batch scoring.

  • Resource monitoring and reporting

    DataRobot’s Resource Monitor feature provides a view of platform usage across the organization, including which workers and models are taking up runtime, enabling effective resource planning.

  • Integrates with Hadoop

    DataRobot uses your Hadoop distribution’s application management services to distribute runtime libraries to Hadoop Data nodes. Working directly with HDFS, running predictions in DataRobot does not require a proprietary storage layer or the movement of data to an edge node. The DataRobot workload runs in YARN containers, so you do not need to partition your cluster to prevent resource conflicts.

  • Works with enterprise data

    No matter where your data resides – relational databases, Hadoop clusters, text files, or other sources – DataRobot quickly and easily connects to your data source.

  • Explainable models

    Users can download DataRobot’s diagnostic charts, data, and documentation to share them with executives, stakeholders, and regulators.

  • Supports advanced security

    DataRobot offers native security for fine-grained role-based authorization and supports Kerberos and LDAP protocols. In Hadoop, it works with your existing encryption policies.

  • Editable rating tables

    Customizable rating tables allow users to edit and manipulate the tables according to their unique business rules, allowing for an optimal blend of machine learning and human experience.

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