What is the difference between the open source and Pro product?

The core framework is Apache 2.0 on GitHub. Pro offers a managed version plus Pro-only features for scaling teams.

ZenML

ZenML | Find AI List

Overview

ZenML is an open-source MLOps framework designed to streamline and standardize AI workflows, from training pipelines to agent evaluations. It provides a unified platform for orchestration, versioning, and governance, enabling teams to accelerate AI delivery. ZenML abstracts away infrastructure complexities, allowing users to define their hardware needs in Python and handle containerization, GPU provisioning, and pod scaling. It integrates with over 60 tools across the AI ecosystem, supporting frameworks like Scikit-learn, LangChain, and PyTorch. ZenML's smart caching and deduplication features reduce compute costs, while its governance and security features ensure compliance and data sovereignty, making it suitable for both individual users and enterprise teams.

Common tasks

Orchestrating machine learning pipelines Versioning artifacts and environments Abstracting infrastructure for ML workflows Caching and deduplicating computations Governing and securing ML pipelines Connecting data retrieval, reasoning, and training steps Deploying ML models to production

FAQ

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What is the difference between ZenML and other machine learning orchestrators?

ZenML doesn't take an opinion on the orchestration layer, allowing you to start writing locally and deploy on any orchestrator. It supports many orchestrators natively and can be extended to work with custom orchestrators.

Does ZenML integrate with my MLOps stack?

Yes! ZenML supports Kubernetes, AWS, GCP Vertex AI, Kubeflow, Apache Airflow, and many more. It also supports artifact, secrets, and container storage for all major cloud providers.

Does ZenML help in GenAI / LLMOps use-cases?

Yes, ZenML is fully compatible and intended for productionalizing LLM applications. There are examples with LlamaIndex, OpenAI, LangChain, and more available.

How can I build my MLOps/LLMOps platform using ZenML?

Start simple with the user guides, then extend with experiment trackers, model deployers, model registries and more from the stack components library.

FAQ+