Transforms AI assistants into a full ML engineering environment for training and fine-tuning models across multiple backends (local GPU, Mistral, Together AI, OpenAI) and cloud providers (Lambda Labs, RunPod, SSH-accessible VPS), with dataset management, experiment tracking, cost estimation, and deployment to Ollama/Open WebUI.
Provides AI agents with structured knowledge about 67+ generative AI models, including model recommendations, prompt formatting, parameter guidance, and validation. It acts as a prompt engineering co-pilot that helps agents use their existing tools more effectively.
Enables AI assistants to interact with MLflow experiments, runs, and registered models. Supports browsing experiments, retrieving run details with metrics and parameters, and querying the model registry through natural language.
Enables AI assistants to perform MLOps workflows such as experiment tracking, model registry, dataset management, pipeline orchestration, and data lineage by wrapping DVC, MLflow, and Git.
Enables AI scientists to access over 1000 machine learning models, datasets, APIs, and scientific packages for data analysis, knowledge retrieval, and experimental design from any large language model.
