ZenML
MLOps
Framework-agnostic pipeline for reproducible ML.
Tier 1
Adoption
Tier 1
Recognition
Tier 3
Security
Tier 2
Reliability
About
Overview
ZenML is a framework-agnostic MLOps platform that enables teams to build, orchestrate, and deploy reproducible ML pipelines and AI agents. It provides a unified execution model for both traditional ML workflows and modern AI agents, with infrastructure abstraction that allows code to run unchanged across different compute environments—from local machines to Kubernetes, Vertex AI, or SageMaker.
Key Features
Pipeline & Agent Orchestration
- Unified workflow orchestration for both ML pipelines and LLM-based agents
- Write pipelines and agents in Python without environment-specific rewrites
- State management, data passing, and termination control work consistently across pipeline and agent workloads
Artifact Management & Versioning
- Automatic versioning of all step results: trained models, datasets, and intermediate artifacts
- Compare artifact versions to identify what changed between runs
- Re-execute pipelines from any step when issues are detected
Infrastructure Abstraction
- Define compute requirements in Python; ZenML handles dockerization, GPU provisioning, and pod scaling
- Support for multiple orchestrators and cloud platforms without code changes
- Run the same pipeline locally for development or on production infrastructure
Caching & Deduplication
- Automatic caching of step results to avoid redundant computation
- Reuse cached outputs when code and inputs remain unchanged
- Reduces compute costs for iterative development
Governance & Security
- Centralized credential and API key management
- Role-based access control (RBAC) with custom roles on Enterprise plans
- Execution trace inspection and lineage auditing
- SSO (SAML/OIDC) and audit logs for Enterprise deployments