Flexibility

Freedom to Choose,
Power to Switch

One framework for all your MLOps and LLMOps needs,
with the flexibility to change as you grow
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Illustration of a cloud-based data science workflow centered around the ZenML logo, surrounded by various cloud computing and data science components including icons for Kubernetes, AWS, Google Cloud, and data scientist profiles. The diagram depicts the interconnected nature of modern cloud-based data science workflows powered by tools like ZenML.

Seamless Backend Interoperability

Liberate your ML pipelines from infrastructure constraints.
  • Effortlessly switch between orchestration engines and artifact stores.
  • Multi-cloud support ensures true vendor independence.
  • Maintain consistent workflows across diverse environments.
An example of a Data Scientist and an ML engineer contributing to a ZenML project using Docker.
An example of a DAG visualizer with a preprocessing and training model and lineage.

Unified ML and GenAI framework

Optimize resource utilization across traditional ML and generative AI workloads.
  • Access cost-effective and readily available GPUs without code modifications.
  • Seamlessly transition between CPU and GPU environments.
  • Leverage unified abstractions for diverse ML and GenAI tasks.

Customizable LLMOps Stack

Tailor your large language model operations to your specific requirements.
  • Automate updating and testing of your RAG applications.
  • Flexible integrations with leading fine-tuning frameworks, Hugging Face Accelerate, Axolotl, PyTorch Lightning and more.
  • Get a central picture of all LLM models with prompts, metrics, and more.
ZenML workflow diagram: data loading, model training, and evaluation steps
Dragos Ciupureanu
With ZenML, we're no longer tied to a single cloud provider. The flexibility to switch backends between AWS and GCP has been a game-changer for our team.
Dragos Ciupureanu
Koble.ai
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