Model Deployment - AIQ Capability Overview
Model Deployment is the process of taking a model that has been selected, trained and integrated and deploying it to...
Model Deployment is the process of taking a model that has been selected, trained and integrated and deploying it to a production environment while meeting versioning, availability, scalability, and security requirements.
To use models in production settings, companies must have the capability to deploy and maintain them while ensuring repeatability, reliability, and quality for end users. Effective deployment strategies help reduce deployment risks which is critical when delivering machine learning solutions at scale.
A model registry is a tool that provides centralized cataloging and versioning of machine learning models and their associated metadata, such as hyperparameters and performance metrics.
MLOps is the intersection of machine learning and DevOps. It's the technology and processes for developing, deploying, maintaining, and automating machine learning models in production environments reliably and efficiently.
Retraining machine learning models is important to help reduce degradation of model performance over time in production environments.
A/B, canary, and multi-armed bandit are statistical approaches for managing releases by directing subsets of end users to different model versions. This helps evaluate model performance between versions and identify potential issues efficiently.