Create and manage ML pipelines with ZenML Dashboard
Upload a machine learning model to Hugging Face Hub
Display leaderboard of language model evaluations
Convert Hugging Face models to OpenVINO format
Track, rank and evaluate open LLMs and chatbots
Evaluate adversarial robustness using generative models
Evaluate model predictions with TruLens
Evaluate reward models for math reasoning
Evaluate AI-generated results for accuracy
Upload ML model to Hugging Face Hub
Explain GPU usage for model training
View NSQL Scores for Models
Display LLM benchmark leaderboard and info
Zenml Server is a dedicated server for managing and optimizing machine learning (ML) workflows. It enables users to create, monitor, and compare ML pipelines efficiently. Designed for scalability and collaboration, Zenml Server is a robust tool for data scientists and ML engineers aiming to streamline their workflow management processes.
• Pipeline Management: Centralized platform for creating and managing ML pipelines.
• Experiment Tracking: Comprehensive tracking of experiments and model performance.
• Version Control: Ability to version pipelines and maintain a clear history of changes.
• Scalability: Built to handle large-scale ML workflows and distributed teams.
• Collaboration Tools: Features to enable teams to work together seamlessly.
• Extensibility: Integrates with popular ML frameworks and tools.
• Integration with ZenML Dashboard: Provides a user-friendly interface for monitoring and managing workflows.
What is the purpose of Zenml Server?
Zenml Server is designed to streamline ML workflow management by providing a centralized platform for creating, monitoring, and optimizing ML pipelines.
How do I integrate Zenml Server with existing tools?
Zenml Server supports integration with popular ML frameworks and tools through its extensible architecture. Refer to the documentation for specific integration steps.
Can Zenml Server be used by large teams?
Yes, Zenml Server is built to scale and includes collaboration features, making it suitable for large and distributed teams.