2 repositorios
Offloading compute requirements for pipeline steps to scalable remote managed infrastructure.
Distinct from Managed Infrastructure Deployment: Distinct from Managed Infrastructure Deployment: focuses on the execution of pipeline steps as managed jobs, not the provisioning of the infrastructure itself.
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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
Runs machine learning pipeline steps as managed cloud jobs to offload compute requirements from local environments to scalable remote infrastructure.
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
Enables switching between local, remote, and cloud infrastructure backends without modifying underlying pipeline code.