Create reproducible ML pipelines with ZenML
Determine GPU requirements for large language models
Evaluate AI-generated results for accuracy
Create and upload a Hugging Face model card
Generate leaderboard comparing DNA models
Calculate survival probability based on passenger details
SolidityBench Leaderboard
Compare and rank LLMs using benchmark scores
Benchmark models using PyTorch and OpenVINO
Measure BERT model performance using WASM and WebGPU
Evaluate model predictions with TruLens
Display LLM benchmark leaderboard and info
Search for model performance across languages and benchmarks
Zenml Server is a powerful tool designed to create reproducible ML pipelines. It serves as a central hub for managing machine learning workflows, experiments, and environments. Built with MLOps principles in mind, Zenml Server helps teams collaborate more effectively and ensures consistent results across different stages of the machine learning lifecycle.
• Pipeline Management: Easily define and manage end-to-end ML workflows.
• Environment Orchestration: Ensure consistency across development, testing, and production environments.
• Experiment Tracking: Monitor and compare different runs of your ML pipelines.
• Collaboration Tools: Share and work on ML projects with team members seamlessly.
• Extensibility: Integrate with popular ML frameworks and tools like TensorFlow, PyTorch, and more.
• Version Control: Track changes and maintain reproducibility of your ML workflows.
• Monitoring & Logging: Gain insights into pipeline performance and debug issues efficiently.
What is Zenml Server used for?
Zenml Server is used to create, manage, and deploy reproducible ML pipelines, ensuring consistency and collaboration across teams.
How does Zenml Server integrate with existing ML frameworks?
Zenml Server supports integration with popular ML frameworks like TensorFlow and PyTorch through its extensible architecture.
Can Zenml Server be deployed in production environments?
Yes, Zenml Server is designed to scale and can be deployed in production to manage and monitor ML workflows effectively.