Create and manage ML pipelines with ZenML Dashboard
Evaluate AI-generated results for accuracy
Quantize a model for faster inference
Evaluate and submit AI model results for Frugal AI Challenge
Compare code model performance on benchmarks
Merge machine learning models using a YAML configuration file
Display and submit language model evaluations
Explore and submit models using the LLM Leaderboard
Find and download models from Hugging Face
Submit deepfake detection models for evaluation
Browse and evaluate ML tasks in MLIP Arena
Measure BERT model performance using WASM and WebGPU
Download a TriplaneGaussian model checkpoint
Zenml Server is a powerful tool designed to create and manage ML pipelines with ease. It serves as the web-based interface for ZenML, allowing users to streamline and organize their machine learning workflows. The server provides a centralized platform to monitor, configure, and optimize ML pipelines, making it an essential component for efficient ML workflow management.
• Pipeline Management: Easily create, edit, and monitor ML pipelines through an intuitive interface.
• Multi-User Support: Collaborate with teams seamlessly with role-based access control.
• Real-Time Monitoring: Track pipeline executions and gain insights into performance metrics.
• Extensibility: Integrate with various tools and frameworks in the ML ecosystem.
• Collaboration Tools: Share pipelines, experiments, and results with team members.
zenml server start.http://localhost:8000 to access the Zenml Dashboard.What is Zenml Server used for?
Zenml Server is used to manage and monitor ML pipelines. It provides a centralized interface for creating, running, and analyzing machine learning workflows.
How do I install Zenml Server?
Zenml Server is part of the ZenML package. You can install it using pip install zenml and then start the server with zenml server start.
Can I use Zenml Server with my existing ML tools?
Yes, Zenml Server supports integration with popular ML tools and frameworks. It is designed to be extensible and compatible with a wide range of machine learning ecosystems.