Create and manage ML pipelines with ZenML Dashboard
Calculate VRAM requirements for LLM models
Explore GenAI model efficiency on ML.ENERGY leaderboard
Evaluate code generation with diverse feedback types
Rank machines based on LLaMA 7B v2 benchmark results
Evaluate and submit AI model results for Frugal AI Challenge
Launch web-based model application
Download a TriplaneGaussian model checkpoint
View and submit language model evaluations
Convert PyTorch models to waifu2x-ios format
View RL Benchmark Reports
Browse and filter ML model leaderboard data
Browse and submit model evaluations in LLM benchmarks
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.