// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Canary Deployment
Canary deployment means releasing a new model version to a small group of users first to test it before a wider rollout.
TECHNICAL DEFINITION
Canary deployment is a progressive rollout strategy where a new model version is deployed to a small subset of production traffic or users, allowing for real-time monitoring and quick rollback if issues arise, minimizing impact.
BACKGROUND
Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Phased rollout
- gradual release
- canary release
USAGE NOTE
Canary deployment reduces the risk associated with deploying new machine learning models.
DEVELOPERS
Organizations developing technology related to Canary Deployment.
Provides a feature flagging platform that enables developers to perform canary deployments and controlled rollouts of new AI features, prompt variations, or model versions by toggling them on for a subset of users.
Offers a feature delivery and experimentation platform that allows for robust canary deployments and A/B testing of AI models, prompt engineering changes, and AI-driven application components in production environments.
Specializes in MLOps and model serving, providing the Seldon Core platform which enables advanced deployment strategies like canary releases and A/B testing for machine learning models, ensuring safe and controlled rollouts of new AI capabilities.
Through its MLflow platform and MLOps capabilities, Databricks helps manage the lifecycle of machine learning models, supporting practices like canary deployments for new model versions or prompt-tuning adjustments in production.
Offers an MLOps platform that facilitates experiment tracking, model evaluation, and deployment management, which is crucial for implementing controlled rollouts and canary deployments of AI models and their associated prompt strategies.
Provides a comprehensive managed service for machine learning, including model deployment endpoints that support A/B testing and traffic splitting, enabling canary deployments for different versions of AI models or prompt engineering strategies.
Offers a unified platform for ML development and deployment, featuring model serving capabilities that allow for traffic management and gradual rollouts, essential for canary deployments of AI models and prompt engineering experiments.
For applications integrating AI models or prompt-generated content into user-facing web experiences, Vercel's deployment and Edge Config features can be used to perform canary releases of new UI/UX elements powered by different AI outputs or prompt designs.