Predict customer churn based on input details
View and compare language model evaluations
Display and submit LLM benchmarks
Evaluate code generation with diverse feedback types
Convert PyTorch models to waifu2x-ios format
View and submit LLM benchmark evaluations
Export Hugging Face models to ONNX
Submit deepfake detection models for evaluation
Demo of the new, massively multilingual leaderboard
Measure BERT model performance using WASM and WebGPU
Compare model weights and visualize differences
Measure over-refusal in LLMs using OR-Bench
Multilingual Text Embedding Model Pruner
Building and deploying a machine learning model using Gradio Application is a straightforward process that allows you to create and share web-based interfaces for your machine learning models. Gradio is an open-source tool that simplifies the deployment of ML models by converting them into APIs or user-friendly web applications. This application focuses on predicting customer churn based on input details, making it a practical example of how to build and deploy ML models effectively.
• Easy-to-use interface: Gradio provides a simple way to create web-based interfaces for your machine learning models.
• Real-time interaction: Users can input data and receive predictions in real-time.
• Customizable UI: You can customize the appearance of your application to suit your needs.
• Deployment options: Deploy your application locally, on a server, or share it directly with others.
• Accessibility: Share your application via a URL, making it accessible to anyone with a web browser.
• Model benchmarking: Compare the performance of different machine learning models.
• Integration with popular ML frameworks: Works seamlessly with libraries like TensorFlow, PyTorch, and Scikit-learn.
pip install gradio.gradio.Blocks or gradio.Space for a more complex layout.What is Gradio used for?
Gradio is used to create and deploy machine learning models as web-based applications. It simplifies the process of sharing your models with non-technical users.
Do I need to know how to code to use this application?
Yes, you need basic programming knowledge in Python to build and deploy machine learning models using Gradio.
Can I use Gradio for deep learning models?
Yes, Gradio supports integration with deep learning frameworks like TensorFlow and PyTorch, making it suitable for deploying complex models.