AI Infrastructure

Pipeline

A pipeline in ML is a sequence of data processing and modeling steps chained together to automate a workflow. ML pipelines include data preprocessing, feature engineering, model training, and evaluation stages.

Understanding Pipeline

A pipeline in machine learning is a structured sequence of data processing and modeling steps chained together so that the output of one stage feeds directly into the next. A typical pipeline might include data ingestion, preprocessing, feature engineering, normalization, model training, evaluation, and deployment. Frameworks like scikit-learn provide pipeline abstractions that ensure consistent transformations across training and inference, preventing common bugs like data leakage. In natural language processing, pipelines often chain tokenization, embedding, and model inference stages together. MLOps platforms extend this concept to production environments, managing pipeline orchestration, versioning, and monitoring at scale. The pipeline paradigm promotes reproducibility, modularity, and code reuse, making it easier for teams to experiment with different components without restructuring entire workflows. Modern AI systems increasingly rely on complex pipelines that integrate multiple models, retrieval systems, and post-processing steps.

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AI Infrastructure

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Related AI Infrastructure Terms

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.