EvalML vs TPOT

Side-by-side — pricing, strengths, tradeoffs, and fit.

EvalML

Automates ML pipeline creation and evaluation with domain-specific functions.

TPOT

Automatically creates and optimizes machine learning pipelines using genetic programming.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
◌ Declining·From $0

Overview

One-liner

Automates ML pipeline creation and evaluation with domain-specific functions.

One-liner

Automatically creates and optimizes machine learning pipelines using genetic programming.

Maturity

Declining

Maturity

Declining

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams needing to automate the creation and optimization of ML pipelines
Projects where domain-specific functions can significantly improve model accuracy

✓ Best for

Data scientists looking to automate pipeline creation and optimization for faster experimentation.
Developers who want to integrate automated ML into their projects without deep knowledge of the underlying algorithms.

Not ideal for

✕ Not for

Developers looking for a cloud-based managed service
Projects requiring real-time data processing capabilities

✕ Not for

Projects requiring real-time or near-real-time model updates, as TPOT's genetic programming approach can be time-consuming.
Teams with limited computational resources, as the optimization process may require significant processing power.

Strengths

↑ Strengths

Automated pipeline creation and optimization
Domain-specific functions for ML tasks
Evaluation of pipelines with various metrics

↑ Strengths

Automates the creation and optimization of machine learning pipelines.
Uses genetic programming to evolve optimal pipelines.
Supports a wide range of scikit-learn compatible models and preprocessing techniques.

Weaknesses

↓ Weaknesses

Limited integrations with non-Python data sources and tools
Performance issues with large datasets

↓ Weaknesses

Steep learning curve for non-Python developers
Limited scalability for large datasets
Poor documentation and community support
Resource-intensive optimization process
Full EvalML profile →Full TPOT profile →