bayaml 0.1.1

Baya – Modular ML Orchestration Framework for structured machine learning workflows.

Baya

Baya is a structured, production-ready Machine Learning orchestration and AutoML framework designed for clear, reproducible, and extensible ML workflows.

It supports both:

• Minimal one-line training
• Fully modular ML pipelines
• AutoML with leaderboard tracking
• Exporting results to multiple formats

Why Baya?

• Multiple abstraction layers (simple → advanced)
• Built-in AutoML engine
• DAG-based orchestration
• Experiment tracking
• Model registry
• CLI interface
• Export system (CSV, JSON, Excel, PDF, DOCX, PNG, etc.)
• Fully compatible with Pandas workflows
• Extensible and modular architecture

Installation

pip install bayaml

Runtime dependencies installed automatically:

• pandas
• numpy
• scikit-learn
• scipy
• pyyaml
• matplotlib

Works With Pandas

Baya integrates directly with Pandas. You can freely mix Pandas + Baya:

import pandas as pd
from baya import quick_train

df = pd.read_csv("data.csv")

# Use pandas normally
df["new_feature"] = df["feature"] * 2

# Pass directly into Baya
metrics = quick_train(
    data=df,
    target="Target",
    model="linear_regression"
)

print(metrics)

You can:

• preprocess with pandas
• clean manually
• engineer features
• then hand over to Baya

No restrictions.

API Layers

Baya supports three levels of control.

1️⃣ Simple API — quick_train()

Minimal lines.

from baya import quick_train

metrics = quick_train(
    data="data.csv",
    target="Target",
    model="linear_regression",
    test_size=0.2
)

Supported data inputs:

• pandas DataFrame
• CSV path
• JSON path
• Excel path

Automatically performs:

• load
• target selection
• split
• model creation
• train
• predict
• evaluate

Returns a metrics dictionary.

2️⃣ Fluent API — Baya Class

Chainable interface.

from baya import Baya

metrics = (
    Baya("data.csv", target="Target")
    .train("linear_regression")
    .evaluate()
)

Also works with a DataFrame:

Baya(df, target="Target").train("logistic_regression").evaluate()

3️⃣ Advanced API — Project

Full modular control.

from baya import Project

project = Project()

project.data.load("data.csv")
project.data.set_target("Target")

project.split.train_test(test_size=0.2)

project.model.create("linear_regression")
project.model.train()

metrics = project.evaluate.evaluate_regressor()

All Core Methods (Advanced API)

Data:

• project.data.load(path_or_dataframe)
• project.data.set_target("column")
• project.data.preview()

Split:

• project.split.train_test(test_size=0.2)

Model:

• project.model.create("linear_regression")
• project.model.train()
• project.model.predict()

Evaluate:

• project.evaluate.evaluate_regressor()
• project.evaluate.evaluate_classifier()
• project.evaluate.custom_metric(...)

Tracking:

• project.tracker.start()
• project.tracker.log_metrics(...)
• project.tracker.finalize()

Export:

• project.export.csv("results.csv")
• project.export.json("results.json")
• project.export.excel("results.xlsx")
• project.export.pdf("report.pdf")
• project.export.docx("report.docx")
• project.export.image("plot.png")

Supported Export Formats

Baya supports exporting to:

• .csv
• .json
• .xlsx
• .pdf
• .docx
• .png
• .jpg

Exports available for:

• metrics
• predictions
• leaderboards
• visualizations
• reports

AutoML

Automatic model selection.

from baya import automl

result = automl(
    data="data.csv",
    target="Target"
)

print(result["best_model"])
print(result["best_score"])

AutoML includes:

• Cross-validation
• Model comparison
• Leaderboard generation
• Best model selection
• Run tracking
• CV results storage

Leaderboard

Stored in: baya_runs/leaderboard.json

Visualize:

from baya.visualize import plot_leaderboard
plot_leaderboard()

Model Registry

from baya import register_model, list_models

register_model("my_model", MyModelClass)
print(list_models())

Built-in models:

• linear_regression
• logistic_regression
• random_forest_classifier
• random_forest_regressor
• svm_classifier
• svm_regressor

Config-Based Reproducibility

workflow.yaml:

data_path: data.csv
target: Target
model: logistic_regression
task: classification
test_size: 0.2
seed: 42

Run:

from baya import Project

project = Project.from_config("workflow.yaml")
metrics = project.run()

CLI Usage

baya info
baya run workflow.yaml
baya automl workflow.yaml
baya registry list-models
baya leaderboard
baya visualize leaderboard

Task Auto Detection

Automatically detects:

• Regression
• Classification

Based on the target variable.

Architecture

Layered Design:

Core Engine
↓
Orchestration (DAG)
↓
Simple API
↓
AutoML
↓
CLI

The Simple API wraps the advanced engine — no duplicated logic.

Optional Dependencies

Install extra features:

Visualization extras:

pip install bayaml[viz]

Deep learning extras:

pip install bayaml[deep]

Development tools:

pip install bayaml[dev]

Branding

baya info

Shows:

• Framework name
• Version
• Author
• Website
• Support link

Branding is non-intrusive.

Developer Setup

git clone <repo>
cd baya
pip install -e .[dev]
pytest

License

MIT License.

About

Baya is built and maintained by Aditya Sarode, focused on scalable AI systems, ML architecture, and production-ready engineering.