Model Serving
Model serving is the process of deploying trained machine learning models to production environments where they can respond to prediction requests. Efficient serving requires optimization for latency, throughput, and cost.
Understanding Model Serving
Model serving is the infrastructure and process of deploying trained machine learning models into production environments where they can receive input data and return predictions in real time or in batch mode. Effective model serving requires careful attention to latency, throughput, scalability, and reliability. Platforms like TensorFlow Serving, TorchServe, and Triton Inference Server provide frameworks for loading models, managing versions, and handling concurrent requests. Model serving infrastructure must account for AI chip utilization, memory management, and autoscaling to meet variable demand. It integrates closely with feature stores for consistent feature retrieval and monitoring systems that track prediction quality over time to detect model drift. In production environments supporting agentic AI or tool use capabilities, model serving must handle complex orchestration of multiple model calls while maintaining low-latency responses for end users.
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.