Machine Learning Infrastructure
Frameworks
A collection of software tools, libraries, and interfaces designed to streamline the creation, training, and deployment of artificial intelligence and machine learning models.
Explanation
AI frameworks serve as the foundational infrastructure for building machine learning applications. They provide pre-written code for common tasks such as tensor manipulation, backpropagation, and optimization algorithms. By abstracting the underlying mathematical complexity and hardware-specific optimizations for CPUs, GPUs, or TPUs, frameworks allow developers to prototype and scale models more efficiently. Modern frameworks often support multiple programming languages, though Python is the most common, and they facilitate the transition from research to production environments. Examples include TensorFlow, PyTorch, and Keras.