Building a T4-Friendly Autonomous Data Science Agent with DeepAnalyze-8B, Sandboxed Code Execution, and Iterative Analysis

This tutorial details the complete pipeline for building an autonomous data science agent: set up a stable runtime on Colab, install ML dependencies, load DeepAnalyze-8B with 4-bit quantization to fit limited VRAM, and add a sandboxed execution environment. The model generates Python code, safely runs it, and observes results, forming an iterative loop. Finally, the agent processes a multi-file e-commerce dataset, automatically performing data cleaning, merging, analysis, visualization, and summarization, producing analyst-level reports. This approach is especially useful for developers using Colab or limited GPUs—4-bit quantization enables DeepAnalyze-8B to run on a T4 (16GB), while sandboxed execution ensures code safety. However, the tutorial does not mention inference speed or actual runtime costs; developers should evaluate practicality for long tasks.