What are AI frameworks?
Artificial intelligence (AI) frameworks consist of datasets, libraries, packages and tools for developing AI systems. This digital ecosystem serves as a structured base for creating tailored AI solutions and customized AI applications.(IBM)
Early AI frameworks started within academic or research communities as a way to support the development of neural networks. As deep learningevolved, the frameworks that followed catered to large-scale AI models and more specific use cases like natural language processing (NLP). AI frameworks also became widely available to novice data scientists, beginner AI developers and enterprises without expertise in machine learning, making AI more accessible.
Components of AI frameworks
AI frameworks have built-in functionalities to help accelerate and simplify machine learning pipelines. While each framework varies, typical elements include:
Packages and libraries for preparing, processing and loading datasets
Implementations of common machine learning algorithms
Preconfigured and pretrained models ready to be fine-tuned or deployed
Methods for model training and model evaluation
Application programming interfaces (APIs) to facilitate AI development in different programming languages
Extensions for optimizing hyperparameters and model performance
Some frameworks offer these advanced features:
Distributed training APIs and libraries for training models across multiple machines or GPUs through parallel computing
MLOps tools for model deployment and monitoring
Visualization tools to better understand models through histograms of weights and biases, plots of learning curves and model predictions, and graphs of metrics like accuracy and loss
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