bayaml 0.1.3

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

Baya

---

🚀 Baya — Lightweight ML Orchestration & AutoML Framework

Baya is a structured Machine Learning orchestration framework for reproducible, automated, and production-ready ML workflows.

It combines:

AutoML

Natural language ML pipelines

Deterministic execution plans

Model deployment

Modular pipeline control

Designed for engineers, researchers, startups, and ML teams.

---

🔎 Why Baya?

Modern ML pipelines are often:

Hard to reproduce

Poorly structured

Over-engineered

Difficult to deploy

Baya provides a clean orchestration layer on top of scikit-learn to make ML:

Reproducible

Structured

Deterministic

Deployable

Without sacrificing flexibility.

---

🧠 Natural Language ML (.auto())

Train models using structured instructions:

from baya import Project

p = Project()

result = p.auto(
    "use https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv "
    "treat tip as target "
    "train regression model using linear regression"
)

print(result)

What Happens Automatically

Dataset loading (CSV, URL, DataFrame)

Target detection

Task detection (classification/regression)

Automatic categorical encoding

Safe pipeline ordering

Train-test split

Model training

Evaluation

Deterministic plan hashing

This makes Baya a lightweight AutoML orchestration engine.

---

📊 Execution Plan Preview

Preview generated pipelines before execution:

plan = p.auto(
    "use data.csv treat price as target train regression model",
    preview=True
)

for step in plan.steps:
    print(step.name)

Baya builds a deterministic execution plan with hashing for reproducibility.

---

⚙️ Core Capabilities

✔ Structured ML Orchestration

Layered architecture wrapping a deterministic execution engine.

✔ Auto Encoding

Categorical columns are automatically encoded before model training.

✔ Auto Task Detection

Classification vs regression inferred automatically.

✔ AutoML Engine

Model comparison, leaderboard tracking, cross-validation.

✔ Model Deployment

Export models for production use.

---

🚀 Deployment

REST Deployment

p.auto(
    "use data.csv treat target as target "
    "train classification model "
    "deploy as rest"
)

Generates a deployable REST bundle.

---

ONNX Export (Edge / C++ Ready)

p.auto(
    "use data.csv treat target as target "
    "train regression model "
    "deploy in c++"
)

Exports model as ONNX.

---

📦 Installation

pip install baya

Dependencies:

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

---

🧩 API Layers

Baya supports three levels of abstraction.

---

1️⃣ Simple API

from baya import quick_train

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

---

2️⃣ Fluent API

from baya import Baya

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

---

3️⃣ Advanced Orchestration API

from baya import Project

project = Project()
project.data.load("data.csv")
project.data.set_target("Target")
project.split.train_test()
project.model.create("linear_regression")
project.model.train()
project.evaluate.evaluate_regressor()

Full modular control.

---

🤖 AutoML

from baya import automl

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

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

Includes:

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

---

📤 Export System

Export metrics, predictions, and reports:

• CSV
• JSON
• Excel (XLSX)
• PDF
• DOCX
• PNG / JPG

---

🏗 Architecture

Core Engine ↓ Execution Plan Builder ↓ Deterministic Plan Hashing ↓ Project API ↓ AutoML / CLI / Simple API

All APIs wrap the same deterministic engine.

No duplicated logic.

---

🔐 Reproducibility

Every .auto() run generates:

• Dataset hash
• Plan hash
• Ordered execution steps

Ensuring reproducible ML workflows.

---

👨‍💻 Developer Setup

git clone https://github.com/adityassarode/baya
cd baya
pip install -e .[dev]
pytest

---

📈 Positioning

Baya is ideal for:

• ML engineers building reproducible pipelines
• Startups needing fast ML deployment
• Data scientists wanting structured automation
• Teams needing deterministic ML workflows

---

📄 License

MIT License

---

👤 Author

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

AutoML

Natural language ML pipelines

Deterministic execution plans

Model deployment

Modular pipeline control

Designed for engineers, researchers, startups, and ML teams.

---

🔎 Why Baya?

Modern ML pipelines are often:

Hard to reproduce

Poorly structured

Over-engineered

Difficult to deploy

Baya provides a clean orchestration layer on top of scikit-learn to make ML:

Reproducible

Structured

Deterministic

Deployable

Without sacrificing flexibility.

---

🧠 Natural Language ML (.auto())

Train models using structured instructions:

from baya import Project

p = Project()

result = p.auto( "use https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv " "treat tip as target " "train regression model using linear regression" )

print(result)

What Happens Automatically

Dataset loading (CSV, URL, DataFrame)

Target detection

Task detection (classification/regression)

Automatic categorical encoding

Safe pipeline ordering

Train-test split

Model training

Evaluation

Deterministic plan hashing

This makes Baya a lightweight AutoML orchestration engine.

---

📊 Execution Plan Preview

Preview generated pipelines before execution:

plan = p.auto( "use data.csv treat price as target train regression model", preview=True )

for step in plan.steps: print(step.name)

Baya builds a deterministic execution plan with hashing for reproducibility.

---

⚙️ Core Capabilities

✔ Structured ML Orchestration

Layered architecture wrapping a deterministic execution engine.

✔ Auto Encoding

Categorical columns are automatically encoded before model training.

✔ Auto Task Detection

Classification vs regression inferred automatically.

✔ AutoML Engine

Model comparison, leaderboard tracking, cross-validation.

✔ Model Deployment

Export models for production use.

---

🚀 Deployment

REST Deployment

p.auto( "use data.csv treat target as target " "train classification model " "deploy as rest" )

Generates a deployable REST bundle.

---

ONNX Export (Edge / C++ Ready)

p.auto( "use data.csv treat target as target " "train regression model " "deploy in c++" )

Exports model as ONNX.

---

📦 Installation

pip install baya

Dependencies:

pandas

numpy

scikit-learn

matplotlib

pyyaml

---

🧩 API Layers

Baya supports three levels of abstraction.

---

1️⃣ Simple API

from baya import quick_train

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

---

2️⃣ Fluent API

from baya import Baya

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

---

3️⃣ Advanced Orchestration API

from baya import Project

project = Project() project.data.load("data.csv") project.data.set_target("Target") project.split.train_test() project.model.create("linear_regression") project.model.train() project.evaluate.evaluate_regressor()

Full modular control.

---

🤖 AutoML

from baya import automl

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

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

Includes:

Cross-validation

Model comparison

Leaderboard generation

Best model selection

Run tracking

---

📤 Export System

Export metrics, predictions, and reports:

CSV

JSON

Excel (XLSX)

PDF

DOCX

PNG / JPG

---

🏗 Architecture

Core Engine ↓ Execution Plan Builder ↓ Deterministic Plan Hashing ↓ Project API ↓ AutoML / CLI / Simple API

All APIs wrap the same deterministic engine.

No duplicated logic.

---

🔐 Reproducibility

Every .auto() run generates:

Dataset hash

Plan hash

Ordered execution steps

Ensuring reproducible ML workflows.

---

👨‍💻 Developer Setup

git clone https://github.com/adityassarode/baya cd baya pip install -e .[dev] pytest

---

📈 Positioning

Baya is ideal for:

ML engineers building reproducible pipelines

Startups needing fast ML deployment

Data scientists wanting structured automation

Teams needing deterministic ML workflows

---

📄 License

MIT License

---

👤 Author

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