SmartML
SmartML is a benchmarking tool for traditional ML and selected DL models.
It is designed to help you compare models fairly on the same dataset using transparent defaults and consistent evaluation rules.
What SmartML Is
SmartML focuses on model comparison, not automation.
It allows you to:
- Run multiple ML and DL models on a dataset
- Compare performance using consistent metrics
- Measure training and inference latency
SmartML prioritizes reproducibility, clarity, and fairness.
What SmartML Is Not
SmartML is intentionally limited.
It is not:
- An AutoML system
- A production training pipeline
- A hyperparameter tuning framework
- A feature engineering search tool
- A Kaggle submission generator
These limitations exist to keep benchmarks honest and comparable.
How SmartML Should Be Used
SmartML is best used as a decision-making tool.
Typical workflow:
- Run SmartML on a dataset
- Identify the best-performing model family
- Exit SmartML
- Build a custom pipeline manually
SmartML helps you choose models, not deploy them.
Core Principles
SmartML is built around a few simple ideas:
- Same data for every model
- Fixed and documented defaults
- No hidden automation
- Clear and inspectable results
If a result cannot be explained, it does not belong in SmartML.
Documentation Overview
This documentation covers:
- How data is encoded before training
- How datasets are split internally
- Which models are supported
- Default parameters used for benchmarking
- Training and evaluation flow
- Benchmarking methodology and metrics
- Known limitations and common questions
Each section exists to answer how and why, not just what.
When to Use SmartML
SmartML is a good fit if you:
- Need quick, fair model comparisons
- Want baseline numbers you can trust
- Are evaluating models for research or experimentation
- Are selecting model families before production work
When Not to Use SmartML
SmartML is not suitable if you:
- Need full control over training pipelines
- Require custom train/test splits
- Want automated feature engineering or tuning
- Are building production-ready systems
Final Note
SmartML is opinionated by design.
If you agree with its assumptions, it will save you time.
If you do not, the code is open and the behavior is explicit.