RapidMiner Studio
RapidMiner Studio
9.8.1

0.0

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RapidMiner Studio free download for Mac

RapidMiner Studio for Mac9.8.1

04 December 2020

Comprehensive data science platform with visual workflow design and full automation.

What is RapidMiner Studio for Mac

RapidMiner Studio is a comprehensive data science platform with visual workflow design and full automation

Features

Visual Workflow Designer

Increase productivity across the entire data science team, from analysts to experts

Connect to Any Data Source

Work with all of your data, no matter where it lives

Automated In-Database Processing

Run data prep and ETL inside databases to keep your data optimized for advanced analytics

Data Visualization & Exploration

Evaluate data health, completeness, and quality

Data Prep & Blending

Eliminate the hassle of preparing data for predictive modeling

Visual & Automated Machine Learning

Quickly create impactful machine learning models without writing code

Model Validation

Understand the true performance of a model before deploying to production

Explainable Models Not Black Boxes

Create visual data science workflows that are easy to explain and easy to understand

Get More From R & Python Code

Scalable code deployment and collaboration between coders and non-coders

Flexible Scoring & Model Operations

Turn predictive insights into business impact

Automation & Process Control

Build sophisticated visual workflows and automate important tasks

Open & Extensible

Integrate with existing applications and code

What's new in RapidMiner Studio

Version 9.8.1:
New Features:
  • Delete Azure Blob Storage Resource
  • Delete Azure Data Lake Storage Resource
  • Delete Azure Data Lake Storage Gen2 Resource
Enhancements:
  • All Loop cloud operators (e.g. Loop Amazon S3, Loop Azure Blob Storage, etc) now only download a file when another operator reads its content. The memory footprint may also decrease by 50%, and unnecessary writes to the disk are avoided.
Bugfixes:
  • Continue RapidMiner Studio start if proxy discovery fails
  • Added missing Cluster attribute to metadata when applying a KMeans model via Apply Model
  • Fixed a regression in Generalized Linear Model (GLM) model training. It again accepts weighted training data
  • Auto Model Clustering showed incorrect results, ignoring training data normalization and attribute reordering
  • Fixed AbstractMethodError when using very old JDBC drivers (built for Java 6 and earlier) to connect to SQL databases
  • Fixes inconsistent parameter order and two unused parameter displayed in parameter panel of Loop Google Storage
  • Fixed result view in open source version
  • Time Series: Fixed spelling errors in help texts
  • Time Series: Fixed missing indices attribute in the meta data of Apply Forecast, if a Function and Seasonal Component Forecast model is used
  • Fixed an issue that could cause connection tests to AI Hubs running behind a federated login via KeyCloak to not properly declare credentials as invalid but instead return a weird error message.

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