3rd-Party AI Development Platform
Jump to navigation
Jump to search
A 3rd-Party AI Development Platform is a software platform that facilitates AI system development (of AI systems).
- Context:
- It can (typically) support the creation of AI Systems.
- It can (typically) provide AI Development Tools, for building AI systems.
- It can (often) integrate AI Testing Frameworks, for achieving tested AI systems.
- It can (often) support AI Deployment Pipelines, for releasing production AI systems.
- ...
- It can range from being a Simple AI Platform to being a Comprehensive AI Platform, depending on its AI development capability coverage.
- It can range from being a Code-Centric AI Platform to being a Visual AI Platform, depending on its reliance on text-based coding or visual elements.
- It can range from being a Standalone AI Platform to being a Cloud-Based AI Platform, based on whether it is installed locally or accessed through a cloud service.
- It can range from being a Proprietary AI Platform to being an Open-Source AI Platform, depending on its licensing and accessibility.
- It can range from being a Single-Domain AI Platform to being a Multi-Domain AI Platform, based on its support for AI domains.
- It can range from being a General-Purpose AI Platform to being a Specialized AI Platform, depending on the scope of its application domains.
- It can range from being a Developer-Focused AI Platform to being a User-Focused AI Platform, depending on its user expertise requirements.
- It can range from being a Basic AI Training Platform to being an Advanced AI Training Platform, depending on its model training capabilitys.
- It can range from being a Research AI Platform to being a Production AI Platform, depending on its deployment readiness.
- ...
- It can integrate AI Development Environment System with AI editing tools, AI debugging systems, and AI visualization tools.
- It can support AI Development Paradigm through AI language features and AI patterns.
- It can connect AI Development Service via AI integration interfaces and AI tool apis.
- It can enable AI Development Team System through AI collaboration tools and AI workspace systems.
- It can target AI Development Domain via domain-specific AI tools and AI workflows.
- It can manage AI Development Deployment using AI release tools and AI environment systems.
- It can enforce AI Development Security through AI compliance systems and AI protection mechanisms.
- It can monitor AI Development Performance using AI analytics tools and AI optimization systems.
- ...
- Example(s):
- AI Model Development Platforms, such as:
- Language Model Development Platforms, such as:
- Foundation Model Platforms, such as:
- Language Service Platforms, such as:
- Computer Vision Development Platforms, such as:
- AutoML Development Platforms, such as:
- AI Research Platforms, such as:
- Edge AI Development Platforms, such as:
- Mobile AI Platforms, such as:
- IoT AI Platforms, such as:
- ...
- Counter-Example(s):
- AI Development Tools, which lack integrated platform capabilitys.
- AI Runtime Platforms, which focus on model execution rather than development.
- AI Data Platforms, which emphasize data management over model development.
- AI Infrastructure Platforms, which provide computing resources without development tools.
- General Software Development Platforms, which do not include specialized tools for AI training or deployment.
- Database Management Platforms, which focus on data storage and query processing rather than AI development.
- Business Intelligence Platforms, which support analytics and reporting but lack capabilities for training AI models.
- Statistical Analysis Platforms, which may assist in data analysis but do not provide full AI development capabilities.
- See: AI Development Environment, AI Framework, AI Model Development System, AI Testing Platform.
References
2024-12-01
- Perplexity.ai
- Question: What are the main categories, deployment options, and development approaches for AI platforms? Include information about current market trends and selection criteria.
- Answer: AI platforms represent diverse technological solutions that enable AI system development and deployment, with variations in their architecture, deployment models, and development approaches.
- Platform Categories:
- Enterprise Solutions:
- Support scalable deployment and system integration capabilities.
- Prioritize security features and data governance.
- Include platforms like Azure AI, Google Cloud AI, and IBM Watson.
- Open-Source Frameworks:
- Enable flexible development and community contribution.
- Support model customization and framework extension.
- Feature systems like TensorFlow, PyTorch, and Keras.
- Specialized Platforms:
- Focus on domain-specific solutions and industry applications.
- Optimize for specific use cases like computer vision.
- Include platforms such as Scale AI and DataRobot.
- Enterprise Solutions:
- Deployment Options:
- Cloud-Based Solutions:
- Provide scalable resources and managed services.
- Present security considerations and vendor dependencies.
- Balance operational expenses with infrastructure benefits.
- On-Premise Solutions:
- Offer enhanced control and security management.
- Require significant investment and ongoing maintenance.
- Support extensive customization capabilities.
- Hybrid Approaches:
- Combine cloud advantages with local control.
- Enable resource optimization across environments.
- Address data sovereignty needs.
- Cloud-Based Solutions:
- Development Approaches:
- Code-Centric Platforms:
- Target experienced developers and engineers.
- Support extensive customization and optimization.
- Enable advanced feature development.
- Visual/Low-Code Platforms:
- Enhance user accessibility and development speed.
- Support business user and citizen developer needs.
- Focus on rapid solution deployment.
- Code-Centric Platforms:
- Market Trends:
- Adoption Patterns:
- Pre-built solution usage at 53% adoption.
- Integration-focused approaches at 34% adoption.
- Growing hybrid deployment preference.
- Selection Considerations:
- Evaluation of technical requirements.
- Assessment of scalability demands.
- Analysis of security needs.
- Alignment with business objectives.
- Adoption Patterns:
- Platform Categories:
- Citations:
[1] https://www.gartner.com/en/documents/3991440 [2] https://www.forrester.com/report/the-state-of-ai-platforms [3] https://www.idc.com/getdoc.jsp?containerId=US47435721