Create reproducible ML pipelines with ZenML
Compare and rank LLMs using benchmark scores
Measure execution times of BERT models using WebGPU and WASM
Create and manage ML pipelines with ZenML Dashboard
View and submit LLM benchmark evaluations
Explore and visualize diverse models
Export Hugging Face models to ONNX
Find recent high-liked Hugging Face models
View and submit language model evaluations
Search for model performance across languages and benchmarks
Evaluate code generation with diverse feedback types
Optimize and train foundation models using IBM's FMS
View RL Benchmark Reports
Zenml Server is a powerful tool designed to create reproducible ML pipelines. It serves as a central hub for managing machine learning workflows, experiments, and environments. Built with MLOps principles in mind, Zenml Server helps teams collaborate more effectively and ensures consistent results across different stages of the machine learning lifecycle.
• Pipeline Management: Easily define and manage end-to-end ML workflows.
• Environment Orchestration: Ensure consistency across development, testing, and production environments.
• Experiment Tracking: Monitor and compare different runs of your ML pipelines.
• Collaboration Tools: Share and work on ML projects with team members seamlessly.
• Extensibility: Integrate with popular ML frameworks and tools like TensorFlow, PyTorch, and more.
• Version Control: Track changes and maintain reproducibility of your ML workflows.
• Monitoring & Logging: Gain insights into pipeline performance and debug issues efficiently.
What is Zenml Server used for?
Zenml Server is used to create, manage, and deploy reproducible ML pipelines, ensuring consistency and collaboration across teams.
How does Zenml Server integrate with existing ML frameworks?
Zenml Server supports integration with popular ML frameworks like TensorFlow and PyTorch through its extensible architecture.
Can Zenml Server be deployed in production environments?
Yes, Zenml Server is designed to scale and can be deployed in production to manage and monitor ML workflows effectively.