autoai
Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
GraphCanon updated 3w · GitHub synced 3w
Decision brief
Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
Good fit when
- Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
- Choose this if your workflow requires Python-based high-quality Jupyter Notebook code generation from experimental models.
Avoid when
- Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing.
- Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (496d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- 12 low (12 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install autoaiSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
BlobCity's AutoAI supports automated machine learning workflows for regression and classification tasks over numerical data. It provides high-quality Python code generation in the form of Jupyter Notebooks.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
``` Python import blobcity as bcSource link
Tags
README
Getting Started
pip install blobcity
import blobcity as bc
model = bc.train(file="data.csv", target="Y_column")
model.spill("my_code.py")
Y_column is the name of the target column. The column must be present within the data provided.
Automatic inference of Regression / Classification is supported by the framework.
Data input formats supported include:
- Local CSV / XLSX file
- URL to a CSV / XLSX file
- Pandas DataFrame
model = bc.train(file="data.csv", target="Y_column") #local file
model = bc.train(file="https://example.com/data.csv", target="Y_column") #url
model = bc.train(df=my_df, target="Y_column") #DataFrame
For agents
This page has a .md twin and JSON over the API.