Plutoshift Features

Based on 10 Plutoshift reviews. Supports a variety of scripting environments

As reported in 10 Plutoshift reviews. Mines data from databases and prepares data for analysis

As reported in 10 Plutoshift reviews. Applies statistical algorithms to selected data

As reported in 10 Plutoshift reviews. Analyzes both structured and unstructured data

As reported in 10 Plutoshift reviews. Interacts with data to prepare it for visualizations and models

Offers modeling capabilities

Creates data visualizations or graphs This feature was mentioned in 10 Plutoshift reviews.

Generates reports of data performance This feature was mentioned in 10 Plutoshift reviews.

As reported in 10 Plutoshift reviews. Unifies information on a singular platform

Consolidate manufacturing information from multiple sources

Structure data using hierarchies to make it easier to use and manage

Ability to trasnfer data between shop floor systems and software products

Provide features for manual and automated data collection

Process manufacturing information to remove errors and duplicated data

Include standard KPIs to monitor manufacturing quality

Deliver real time data on manufacturing operations and processes

Customizable dashboards display KPIs and metrics in real time

Run detailed queries for searching, sorting, and analyzing data.

Create personalized dashboards for observing and interacting with data.

Contains tools to visualize data sets or workflows.

Offers actionable insights or relevant findings from unstructured data sets.

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Allows users to manage model artifacts and tracks which models are deployed in production.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Provisions users based on authorization to both deploy and iterate upon machine learning models.

Provides useful pre-defined data connections to various types of IoT sensors and objects.

Provides a robust external interface for integrating data, logic and workflows with other software applications.

Maintains an efficient, reliable data flow with established sources.

Track the individual performance of connected devices.

Learn about overall operational performance from device observations.

Make observations about environmental and workplace conditions.

Measure the usage of and/or demand for various resources.

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Anticipates needs and offers suggestions without prompting