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Red Hat OpenShift Data Science Features
What are the features of Red Hat OpenShift Data Science?
Model Development
- Language Support
- Drag and Drop
- Pre-Built Algorithms
- Model Training
- Pre-Built Algorithms
- Model Training
- Feature Engineering
Machine/Deep Learning Services
- Computer Vision
- Computer Vision
Deployment
- Application
- Managed Service
- Application
- Scalability
System
- Language Support
- Drag and Drop
Top Rated Red Hat OpenShift Data Science Alternatives
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Red Hat OpenShift Data Science Categories on G2
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Model Development
Language Support | As reported in 23 Red Hat OpenShift Data Science reviews. Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript | 88% (Based on 23 reviews) | |
Drag and Drop | Based on 23 Red Hat OpenShift Data Science reviews. Offers the ability for developers to drag and drop pieces of code or algorithms when building models | 88% (Based on 23 reviews) | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development 23 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 87% (Based on 23 reviews) | |
Model Training | Based on 23 Red Hat OpenShift Data Science reviews. Supplies large data sets for training individual models | 86% (Based on 23 reviews) | |
Pre-Built Algorithms | Provides users with pre-built algorithms for simpler model development 22 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 85% (Based on 22 reviews) | |
Model Training | Supplies large data sets for training individual models 22 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 86% (Based on 22 reviews) | |
Feature Engineering | Transforms raw data into features that better represent the underlying problem to the predictive models This feature was mentioned in 23 Red Hat OpenShift Data Science reviews. | 88% (Based on 23 reviews) |
Machine/Deep Learning Services
Computer Vision | Offers image recognition services 22 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 85% (Based on 22 reviews) | |
Natural Language Processing | Offers natural language processing services 20 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 83% (Based on 20 reviews) | |
Natural Language Generation | As reported in 20 Red Hat OpenShift Data Science reviews. Offers natural language generation services | 86% (Based on 20 reviews) | |
Artificial Neural Networks | Offers artificial neural networks for users This feature was mentioned in 20 Red Hat OpenShift Data Science reviews. | 83% (Based on 20 reviews) | |
Computer Vision | Offers image recognition services 22 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 88% (Based on 22 reviews) | |
Natural Language Understanding | Based on 20 Red Hat OpenShift Data Science reviews. Offers natural language understanding services | 86% (Based on 20 reviews) | |
Natural Language Generation | Offers natural language generation services 20 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 86% (Based on 20 reviews) | |
Deep Learning | Provides deep learning capabilities This feature was mentioned in 21 Red Hat OpenShift Data Science reviews. | 87% (Based on 21 reviews) |
Deployment
Managed Service | Manages the intelligent application for the user, reducing the need of infrastructure This feature was mentioned in 22 Red Hat OpenShift Data Science reviews. | 86% (Based on 22 reviews) | |
Application | Allows users to insert machine learning into operating applications 22 reviewers of Red Hat OpenShift Data Science have provided feedback on this feature. | 88% (Based on 22 reviews) | |
Scalability | As reported in 22 Red Hat OpenShift Data Science reviews. Provides easily scaled machine learning applications and infrastructure | 85% (Based on 22 reviews) | |
Managed Service | Based on 23 Red Hat OpenShift Data Science reviews. Manages the intelligent application for the user, reducing the need of infrastructure | 88% (Based on 23 reviews) | |
Application | Based on 23 Red Hat OpenShift Data Science reviews. Allows users to insert machine learning into operating applications | 87% (Based on 23 reviews) | |
Scalability | As reported in 23 Red Hat OpenShift Data Science reviews. Provides easily scaled machine learning applications and infrastructure | 86% (Based on 23 reviews) |
System
Data Ingestion & Wrangling | As reported in 22 Red Hat OpenShift Data Science reviews. Gives user ability to import a variety of data sources for immediate use | 89% (Based on 22 reviews) | |
Language Support | Supports programming languages such as Java, C, or Python. Supports front-end languages such as HTML, CSS, and JavaScript This feature was mentioned in 23 Red Hat OpenShift Data Science reviews. | 91% (Based on 23 reviews) | |
Drag and Drop | Offers the ability for developers to drag and drop pieces of code or algorithms when building models This feature was mentioned in 23 Red Hat OpenShift Data Science reviews. | 87% (Based on 23 reviews) |
Generative AI
AI Text Generation | Allows users to generate text based on a text prompt. | Not enough data | |
AI Text Summarization | Condenses long documents or text into a brief summary. | Not enough data | |
AI Text-to-Image | Provides the ability to generate images from a text prompt. | Not enough data |
Agentic AI - Data Science and Machine Learning Platforms
Autonomous Task Execution | Capability to perform complex tasks without constant human input | Not enough data | |
Multi-step Planning | Ability to break down and plan multi-step processes | Not enough data | |
Cross-system Integration | Works across multiple software systems or databases | Not enough data | |
Adaptive Learning | Improves performance based on feedback and experience | Not enough data | |
Natural Language Interaction | Engages in human-like conversation for task delegation | Not enough data | |
Proactive Assistance | Anticipates needs and offers suggestions without prompting | Not enough data | |
Decision Making | Makes informed choices based on available data and objectives | Not enough data |
