3rd-Party AI Development Platform

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.
  • ...
• Examples:
  • AI Model Development Platforms, such as:
    • General AI Platforms, such as:
      • TensorFlow (2024) for comprehensive AI system development.
      • PyTorch (2024) for research-oriented AI development.
    • Enterprise AI Platforms, such as:
      • SageMaker (2024) for cloud-based AI development.
      • Azure ML (2024) for enterprise AI solution development.
  • Language Model Development Platforms, such as:
    • Foundation Model Platforms, such as:
      • Hugging Face (2024) for pre-trained model development.
      • OpenAI Platform (2024) for large language model integration.
    • Language Service Platforms, such as:
      • Google Cloud NLP (2024) for natural language processing.
      • Azure Language Studio (2024) for language service development.
  • Computer Vision Development Platforms, such as:
    • Vision Model Platforms, such as:
      • Roboflow (2024) for vision model development.
      • Clarifai (2024) for visual AI development.
  • AutoML Development Platforms, such as:
    • Enterprise AutoML Platforms, such as:
      • DataRobot (2024) for automated model development.
      • H2O.ai (2024) for automated AI solution development.
  • AI Research Platforms, such as:
    • Academic AI Platforms, such as:
      • Colab Pro (2024) for collaborative AI research.
      • Kaggle Platform (2024) for data science competition.
    • Research Lab Platforms, such as:
      • Weights & Biases (2024) for experiment tracking.
      • Neptune.ai (2024) for research collaboration.
  • Edge AI Development Platforms, such as:
    • Mobile AI Platforms, such as:
      • TensorFlow Lite (2024) for mobile AI development.
      • Core ML (2024) for iOS AI development.
    • IoT AI Platforms, such as:
      • Edge Impulse (2024) for embedded AI development.
      • Azure IoT Edge (2024) for edge device AI.
  • ...
• Counter-Examples:
  • 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.

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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.
    • 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.
    • 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.
    • 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.
  • 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

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