Introduction to Modal
Modal is a serverless platform designed specifically for AI and data teams, providing high-performance AI infrastructure. It allows users to bring their own code and run CPU, GPU, and data-intensive compute at scale. Key features include:
- Serverless Compute: Users can run functions in the cloud without managing infrastructure.
- GPU and CPU Support: Supports both GPU and CPU for diverse computational needs.
- Automatic Scaling: Resources scale automatically based on demand, ensuring efficiency.
- Flexible Environments: Users can deploy custom AI models and fine-tune them without hassle.
- Seamless Integrations: Easily integrates with existing workflows and tools.
- Data Storage Solutions: Offers solutions for data storage and management.
- Job Scheduling: Users can schedule jobs for batch processing and other tasks.
- Web Endpoints: Provides web endpoints for easy access to deployed models.
- Built-in Debugging: Features built-in debugging tools to streamline development.
Use Cases
- Serve Custom AI Models at Scale: Ideal for deploying and managing AI models in production.
- Generative AI Inference: Supports generative AI applications with high efficiency.
- Fine-tuning and Training: Allows for fine-tuning models without the need for infrastructure management.
- Batch Processing: Optimized for high-volume workloads, making it suitable for data-intensive tasks.
- Language Models: Supports various language models for NLP tasks.
- Image, Video, and 3D Audio Processing: Capable of handling multimedia processing tasks.
- Sandboxed Code: Provides a secure environment for running code.
- Computational Bio: Useful for bioinformatics and computational biology applications.
