Create and manage ML pipelines with ZenML Dashboard
GIFT-Eval: A Benchmark for General Time Series Forecasting
Browse and submit evaluations for CaselawQA benchmarks
Browse and submit LLM evaluations
Evaluate model predictions with TruLens
Compare code model performance on benchmarks
Convert PyTorch models to waifu2x-ios format
Visualize model performance on function calling tasks
View and submit language model evaluations
Explain GPU usage for model training
Persian Text Embedding Benchmark
Measure over-refusal in LLMs using OR-Bench
Download a TriplaneGaussian model checkpoint
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.