3 مستودعات
Systems for injecting dynamic inputs into pipeline templates to control execution flow.
Distinct from Execution Parameter Restrictions: None of the candidates cover general CI/CD pipeline parameter injection; they focus on AI tools or AWS API restrictions.
Explore 3 awesome GitHub repositories matching devops & infrastructure · Pipeline Parameterization. Refine with filters or upvote what's useful.
Pipeline is a Kubernetes native CI/CD framework and cloud native pipeline orchestrator. It functions as a custom resource controller that translates declarative pipeline definitions into coordinated pod executions and managed workloads. The system acts as a containerized task runner, allowing for the execution of standalone build steps and reusable tasks that process specific inputs to produce defined outputs. It enables the orchestration of complex workflows by running a sequence of independent containers as modular components within a cloud environment. The platform covers automated softwa
Injects dynamic inputs into predefined task and pipeline templates to control the execution flow of each run.
ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself
Creates immutable records of pipeline code, configuration, and container images to ensure reproducibility.
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Creates immutable records of pipeline code, configuration, and container images to ensure consistent and reproducible execution.