// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
CI/CD
CI/CD refers to practices that automate the building, testing, and deployment of software, ensuring faster and more reliable updates.
TECHNICAL DEFINITION
CI/CD (Continuous Integration/Continuous Delivery) is a set of practices that automate the integration of code changes, execution of tests, and deployment of applications, extending to MLOps for continuous model integration and delivery.
BACKGROUND
The Easy Approach to Requirements Syntax (EARS) is a structured method for writing natural language requirements using a small set of keywords and sentence patterns. Developed by Alistair Mavin and colleagues at Rolls-Royce plc while analysing airworthiness regulations for a jet engine control system, EARS was first published at the IEEE International Requirements Engineering Conference (RE'09) in 2009. EARS gently constrains free-form natural language by imposing a consistent clause order and a limited vocabulary of structural keywords, reducing or eliminating common problems such as ambiguity, vagueness and incompleteness.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Continuous Integration
- Continuous Delivery
- DevOps automation
USAGE NOTE
Implementing CI/CD in MLOps speeds up the iteration cycle for machine learning models.
DEVELOPERS
Organizations developing technology related to CI/CD.
Offers a platform and tools for building, training, and deploying AI models and applications, facilitating CI/CD for models, datasets, and prompt engineering artifacts through version control, Spaces, and integrations.
Provides an open-source platform for managing the machine learning lifecycle, including experiment tracking, reproducible runs, and model deployment, crucial for CI/CD in AI engineering.
An open-source system for versioning data and models, enabling reproducible ML pipelines and integrating with standard CI/CD tools for automated testing and deployment of AI components.
Offers an MLOps platform for tracking, visualizing, and optimizing machine learning models, providing features for artifact versioning and pipeline orchestration essential for CI/CD in AI development.
A comprehensive platform for building, deploying, and managing machine learning models, featuring robust MLOps capabilities, including CI/CD pipelines for automating model training, evaluation, and deployment.
Provides a fully managed service for developing, training, and deploying machine learning models at scale, offering MLOps features to implement CI/CD for AI engineering workflows.
An open-source tool specifically designed to implement CI/CD for machine learning projects, enabling automated model training, testing, and deployment directly from Git workflows.
Microsoft's cloud-based platform for the end-to-end machine learning lifecycle, including MLOps features that support CI/CD for AI model development and deployment.