Predict customer churn based on input details
Convert Hugging Face models to OpenVINO format
Evaluate adversarial robustness using generative models
Upload ML model to Hugging Face Hub
Display leaderboard of language model evaluations
Evaluate model predictions with TruLens
Find and download models from Hugging Face
Explain GPU usage for model training
Display leaderboard for earthquake intent classification models
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Text-To-Speech (TTS) Evaluation using objective metrics.
Display and submit LLM benchmarks
Browse and evaluate ML tasks in MLIP Arena
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.