auto_ml
Automated machine learning for production and analytics.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Verified · Jul 12, 2026Overview
What is auto_ml?
Automates the process of building machine learning models with support for NLP, XGBoost, CatBoost, LightGBM. Outputs production-ready code and detailed dataset analysis.
Key differentiator
“auto_ml stands out with its comprehensive automation capabilities for machine learning workflows, making it easier to deploy models and analyze datasets without deep expertise.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Automated process restricts fine-tuning and manual adjustments, limiting flexibility in model development
Processing time increases exponentially with dataset size, impacting scalability for big data applications
Fit analysis
Who is it for?
✓ Best for
Teams needing quick setup of machine learning pipelines with minimal manual intervention
Data scientists looking to automate repetitive tasks in ML workflows
Projects requiring detailed analytics alongside model outputs
✕ Not a fit for
Real-time applications where immediate feedback is critical
Scenarios requiring highly customized or specialized algorithms not covered by the tool's current support
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with auto_ml
Step-by-step setup guide with code examples and common gotchas.