EpochGo 发布本地优先多智能体 AutoML 引擎，用离线 Ollama 驱动 Scikit-Learn 流水线

Hello r/MachineLearning,

I wanted to share EpochGo, an open-source, local-first AutoML system built to tackle two major bottlenecks in applied machine learning:

1. Cloud API inference costs and data privacy risks: Traditional agentic ML frameworks rely heavily on cloud APIs (GPT-4), creating recurring costs and risking tabular data leakage (especially critical under enterprise compliance and data protection laws like DPDP/GDPR).

2. LLM hallucinations in mathematical reasoning: LLMs are notorious for failing at linear algebra, hyperparameter optimization, and gradient descent calculations when generating raw training logic.

Architecture

https://i.redd.it/znmjaciji1wh1.gif

EpochGo separates the reasoning layer from the execution layer:

• Local LLM as Orchestrator: Powered by Ollama (`qwen2.5-coder:7b`), the agent inspects column distributions, cardinality, and missingness to draft feature engineering and modeling strategies.
• Deterministic Scikit-Learn Execution: All transformations and model fits are executed via pure Scikit-Learn `Pipeline` objects. The LLM does not touch raw data rows; it only reasons over schema metadata.
• AST Validation & Safety: Generated Python code is parsed into Abstract Syntax Trees before execution to enforce safety, preventing arbitrary system commands or data leakage outside the sandbox.
• Dynamic Serving: Once optimal pipelines (XGBoost, Random Forest, Logistic Regression) are evaluated with isolated K-Fold cross-validation, the engine auto-generates a containerized FastAPI endpoint for immediate inference.

The engine runs 100% offline on standard consumer hardware (Mac/Linux/Windows with 16GB RAM) with zero cloud compute cost.

Code & Architecture: https://github.com/AryanDinakaran/EpochGo

Would love your feedback on the pipeline isolation design and any edge cases in handling high-cardinality categorical data offline!