Miscellaneous
Deployment
ML deployment involves serving a trained model to handle real requests: containerizing the model, setting up inference endpoints, managing scaling, monitoring for drift and failures, and maintaining model versions. MLOps practices systematize this lifecycle.
Deployment challenges include latency requirements, cost optimization (batching, quantization), model versioning, A/B testing new model versions, and monitoring for data drift or degraded performance over time.
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Related Terms
Techniques & Methods
Inference
Using a trained AI model to generate predictions or responses on new, unseen data.
Model Components
API (Application Programming Interface)
Interface that allows software applications to communicate and share functionality with each other.
Applications
Enterprise AI
Application of AI technologies to improve business processes, efficiency, and decision-making.
Miscellaneous
Sandbox Environment
Isolated testing environment where code or AI models can run safely without affecting production systems.