Create and manage ML pipelines with ZenML Dashboard
View and submit LLM benchmark evaluations
View and compare language model evaluations
Measure BERT model performance using WASM and WebGPU
Display and filter leaderboard models
Submit deepfake detection models for evaluation
Text-To-Speech (TTS) Evaluation using objective metrics.
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Compare audio representation models using benchmark results
Multilingual Text Embedding Model Pruner
Submit models for evaluation and view leaderboard
Evaluate model predictions with TruLens
Display and submit language model evaluations
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.