AI Development Tools

Software frameworks, libraries, platforms, and integrated development environments designed to facilitate the creation, training, deployment, and maintenance of artificial intelligence systems. Includes deep learning frameworks (TensorFlow, PyTorch, JAX), AutoML platforms, model optimisation toolkits, MLOps infrastructure, and AI-assisted IDEs. Modern tools emphasise reproducibility, scalability, experiment tracking, and continuous delivery of AI systems.

Semantic Classification

Content

Key Characteristics

• Provides high-level APIs for model definition and training

• Supports distributed computing and GPU acceleration

• Integrates visualization and debugging capabilities

• Offers pre-trained models and transfer learning support

• Facilitates continuous integration and deployment (CI/CD) for AI systems

  Overview

  AI Development Tools comprise the software frameworks, libraries, platforms, and integrated development environments designed to facilitate the creation, training, deployment, and maintenance of artificial intelligence systems. These tools include deep learning frameworks (TensorFlow, PyTorch, JAX), AutoML platforms, model optimization toolkits, MLOps infrastructure, and specialized IDEs with AI-assisted coding capabilities. Modern AI development tools emphasize reproducibility, scalability, and accessibility, enabling both researchers and practitioners to build sophisticated AI applications efficiently.

  Related Concepts

• Deep Learning Framework

• MLOps

• AutoML

• Model Deployment

  References

• Abadi, M. et al. (2016). TensorFlow: A System for Large-Scale Machine Learning. OSDI 2016.

• Paszke, A. et al. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019.

• Mao, H. et al. (2023). AI for System and System for AI: Bridging the Gap. ACM Computing Surveys.

Provenance