NewgenONE Platform
AI-first Research and Collaboration Hub
Provides a centralized hub that fosters collaboration, experimentation, and knowledge sharing, enabling teams to accelerate research and improve model development outcomes.
Foster enterprise-wide collaboration among multiple stakeholders by seamlessly sharing assets, documents, research papers, etc. Easily understand each operation through comprehensive context-aware help options available at every step. Develop a long-term enterprise-wide knowledge repository for artificial intelligence models.
AI-driven Research and Collaboration Capabilities of NewgenONE Platform
Enterprise-grade Collaboration Hub
Embedded Contextual Guidance
Advanced Research and Knowledge Management Hub
Enterprise-grade Collaboration Hub
- Collaborate easily with multiple stakeholders, including data science project managers, data engineers, data scientists, and validation engineers for providing end-to-end data science project lifecycle
- Manage projects in your personal user workspace and share assets to collaborate with other stakeholders
- Perform several tasks, such as running, debugging, scheduling, editing, etc., of model pipeline
Embedded Contextual Guidance
- Use the context-aware comprehensive help documentation for assistance at any step
- Leverage intuitive, intelligent user interface with in-line tooltips for each operation and parameter, enabling users to make faster, more accurate inputs and decisions while working within complex workflows
Advanced Research and Knowledge Management Hub
- Collaborate with other stakeholders to publish, share, and review documents, such as articles, research papers, project documents, etc.
- Ensure smooth collaboration with in-built user messaging and knowledge repository
- Manage projects in a personalized way using ‘My Projects’. Create multiple data science experiment pipelines on a single project
- Develop a long-term enterprise-wide knowledge repository for artificial intelligence models
Enterprise-grade Collaboration Hub
- Collaborate easily with multiple stakeholders, including data science project managers, data engineers, data scientists, and validation engineers for providing end-to-end data science project lifecycle
- Manage projects in your personal user workspace and share assets to collaborate with other stakeholders
- Perform several tasks, such as running, debugging, scheduling, editing, etc., of model pipeline
Embedded Contextual Guidance
- Use the context-aware comprehensive help documentation for assistance at any step
- Leverage intuitive, intelligent user interface with in-line tooltips for each operation and parameter, enabling users to make faster, more accurate inputs and decisions while working within complex workflows
Advanced Research and Knowledge Management Hub
- Collaborate with other stakeholders to publish, share, and review documents, such as articles, research papers, project documents, etc.
- Ensure smooth collaboration with in-built user messaging and knowledge repository
- Manage projects in a personalized way using ‘My Projects’. Create multiple data science experiment pipelines on a single project
- Develop a long-term enterprise-wide knowledge repository for artificial intelligence models
