JAX
JAX is a Google-developed numerical computing library that combines NumPy-like syntax with automatic differentiation and GPU/TPU acceleration. JAX is increasingly popular for high-performance machine learning research.
Understanding JAX
JAX is a high-performance numerical computing library developed by Google that combines NumPy-like syntax with automatic differentiation, GPU/TPU acceleration, and just-in-time compilation via XLA. Unlike TensorFlow or PyTorch, JAX takes a functional programming approach, making it particularly well-suited for research that requires composable transformations like vectorization, parallelization, and differentiation. Researchers at Google DeepMind have used JAX to train large-scale models including Gemini and AlphaFold, leveraging its ability to efficiently distribute computations across thousands of TPU cores. JAX's transformations like `jit`, `grad`, `vmap`, and `pmap` allow developers to write simple code that automatically runs fast on accelerator hardware. While it has a steeper learning curve than PyTorch for beginners, JAX has become a preferred framework for cutting-edge machine learning research and scientific computing.
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.