Create reproducible ML pipelines with ZenML
Find and download models from Hugging Face
Text-To-Speech (TTS) Evaluation using objective metrics.
Merge Lora adapters with a base model
Rank machines based on LLaMA 7B v2 benchmark results
Upload ML model to Hugging Face Hub
Browse and filter machine learning models by category and modality
Persian Text Embedding Benchmark
Explore and submit models using the LLM Leaderboard
Display benchmark results
Analyze model errors with interactive pages
Calculate memory usage for LLM models
Convert Hugging Face model repo to Safetensors
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.