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.
Understanding Feature Store
A feature store is a centralized platform for managing, storing, and serving the engineered features used in machine learning models, ensuring consistency between training and inference pipelines. Without a feature store, teams often duplicate feature engineering efforts across projects, leading to inconsistencies and wasted compute resources. Popular feature store solutions like Feast, Tecton, and Amazon SageMaker Feature Store provide APIs for registering features, tracking lineage, and serving precomputed values with low latency during model serving. Feature stores connect closely with data warehouses as upstream data sources and help maintain ground truth integrity by versioning feature transformations. They are an essential component of mature MLOps infrastructure, enabling teams to share reusable features across models, reduce training-serving skew, and accelerate the development of production AI systems.
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 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.