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
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.
Why does Model Deployment matter?
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.
Software Development Lifecycle
Defined delivery framework for planning, developing, testing, deploying, and maintaining machine learning models while ensuring repeatability, quality, and reliability.
My organization uses a model registry to version and manage machine learning models across the modeling lifecycle.
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.
My organization integrates automated testing in the deployment pipeline to verify machine learning model performance and quality.
My organization understands and applies MLOps processes for maintaining and deploying models.
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.
My organization uses automated retraining for continuous model improvement against new data.
Retraining machine learning models is important to help reduce degradation of model performance over time in production environments.
Deployment Environments
Using robust, standard environments for deploying machine learning models to production with strategies to mitigate deployment risk.
My organization uses staged deployment environments (dev, test, prod) for managing machine learning models and mitigating deployment risks.
My organization applies advanced deployment strategies such as A/B, canary, or multi-armed bandit to strategically evaluate model changes.
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.