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.
Understanding Data Pipeline
A data pipeline is an automated sequence of processes that collects, transforms, validates, and delivers data from source systems to destinations where it can be used for analysis or machine learning. In AI applications, pipelines handle everything from raw data ingestion and cleaning to feature engineering, data augmentation, and feeding processed batches into model training loops. Tools like Apache Airflow, Prefect, and Kubeflow Pipelines orchestrate these steps reliably and at scale. A well-designed data pipeline ensures reproducibility, monitors for data drift, and supports versioning so that experiments can be traced back to specific data snapshots. Pipelines are central to MLOps practices, enabling teams to iterate on models quickly while maintaining data quality, lineage, and compliance across the entire machine learning lifecycle.
Category
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 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.
Feature Store
A feature store is a centralized repository for storing, managing, and serving machine learning features. It enables feature reuse, consistency between training and serving, and collaboration across ML teams.