TensorFlow
TensorFlow is an open-source machine learning framework developed by Google that provides tools for building and deploying ML models. It supports distributed training, mobile deployment, and production serving.
Understanding TensorFlow
TensorFlow is an open-source deep learning framework developed by Google Brain that provides a comprehensive ecosystem for building, training, and deploying machine learning models. It supports everything from research prototyping to production deployment across servers, mobile devices, and edge hardware through TensorFlow Lite and TensorFlow.js. The framework offers both high-level APIs through Keras for rapid model development and low-level operations for custom architectures. TensorFlow's computational graph approach enables automatic differentiation for backpropagation and efficient distributed training across multiple GPUs and TPUs. While PyTorch has gained popularity in research settings, TensorFlow remains widely used in production environments, particularly for serving models at scale. The TensorFlow Extended (TFX) platform provides end-to-end tools for data validation, model analysis, and deployment pipelines.
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AI Infrastructure
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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.