Roadmap
The following is a comprehensive list of tasks and features we plan to implement in the MLE-agent project. We've organized these into several categories to provide a clear overview of our development roadmap. We welcome community input and suggestions for new features or improvements!
We encourage our community to contribute ideas, report issues, or even submit pull requests to help us achieve these goals. Your input is invaluable in shaping the future of MLE-agent!
🔨 General Features
- Understand users' requirements to create an end-to-end AI project
- Suggest the SOTA data science solutions by using the web search
- Plan the ML engineering tasks with human interaction
- Execute the code on the local machine/cloud, debug and fix the errors
- Leverage the built-in functions to complete ML engineering tasks
- Interactive chat: A human-in-the-loop mode to help improve the existing ML projects
- Kaggle mode: to finish a Kaggle task without humans
- Summary and reflect the whole ML/AI pipeline
- Integration with Cloud data and testing and debugging platforms
- Local RAG support to make personal ML/AI coding assistant
- Function zoo: generate AI/ML functions and save them for future usage
⭐ More LLMs and Serving Tools
- Ollama LLama3
- OpenAI GPTs
- Anthropic Claude 3.5 Sonnet
💖 Better user experience
- CLI Application
- Web UI
- Discord
🧩 Functions and Integrations
- Local file system
- Local code exectutor
- Arxiv.org search
- Papers with Code search
- General keyword search
- Github Integration
- Google Calendar
- Hugging Face
- SkyPilot cloud deployment
- Snowflake data
- AWS S3 data
- Databricks data catalog
- Wandb experiment monitoring
- MLflow management
- DBT data transform