AutoML

Train machine learning models automatically using AutoGluon integration.

Overview

Strongly's AutoML automatically:

• Selects the best algorithms for your data
• Performs hyperparameter tuning
• Handles feature engineering
• Produces a ranked leaderboard of models

Basic Usage

import pandas as pd
from strongly_python.mlops import automl

# Load your data
df = pd.read_csv("data.csv")

# Create AutoML job
job = automl.create_job(
    name="my-model",
    data=df,
    target_column="label",
    problem_type="binary",
    time_limit=300  # 5 minutes
)

# Wait for completion
job.wait()

# View results
print(job.get_leaderboard())

Creating Jobs

From DataFrame

Upload data directly from a pandas DataFrame:

import pandas as pd
from strongly_python.mlops import automl

df = pd.DataFrame({
    "feature1": [1.0, 2.0, 3.0, 4.0, 5.0],
    "feature2": [0.1, 0.2, 0.3, 0.4, 0.5],
    "target": [0, 0, 1, 1, 1]
})

job = automl.create_job(
    name="churn-prediction",
    data=df,
    target_column="target",
    problem_type="binary",
    time_limit=300
)

From Volume Path

Use data stored in your workspace volume:

job = automl.create_job(
    name="sales-forecast",
    volume_path="/project/data/sales.csv",
    target_column="revenue",
    problem_type="regression",
    time_limit=600
)

Problem Types

Type	Description	Use Case
binary	Binary classification	Yes/No, True/False predictions
multiclass	Multi-class classification	Category prediction
regression	Continuous value prediction	Price, quantity forecasting

# Binary classification
job = automl.create_job(
    name="churn-model",
    data=df,
    target_column="churned",
    problem_type="binary"
)

# Multi-class classification
job = automl.create_job(
    name="category-model",
    data=df,
    target_column="category",
    problem_type="multiclass"
)

# Regression
job = automl.create_job(
    name="price-model",
    data=df,
    target_column="price",
    problem_type="regression"
)

Presets

Control the quality/speed trade-off with presets:

Preset	Description
best_quality	Maximum accuracy, longest training
high_quality	High accuracy, moderate training time
medium_quality	Balanced accuracy and speed
optimize_for_deployment	Fast inference, smaller models

# For highest accuracy
job = automl.create_job(
    name="production-model",
    data=df,
    target_column="label",
    problem_type="binary",
    time_limit=3600,
    preset="best_quality"
)

# For fast deployment
job = automl.create_job(
    name="edge-model",
    data=df,
    target_column="label",
    problem_type="binary",
    time_limit=300,
    preset="optimize_for_deployment"
)

Job Management

Monitoring Progress

# Check status
print(f"Status: {job.status}")
print(f"Job ID: {job.job_id}")

# Wait for completion
job.wait()

# Or poll manually
import time
while job.status == "running":
    print(f"Status: {job.status}")
    time.sleep(30)
    job.refresh()

Getting Results

# Get model leaderboard
leaderboard = job.get_leaderboard()
for i, entry in enumerate(leaderboard[:5]):
    print(f"{i+1}. {entry.model_name}: score={entry.score:.4f}")

# Get best model info
best = job.get_best_model()
print(f"Best Model: {best.model_name}")
print(f"Score: {best.score:.4f}")

# Download model for deployment
model_path = job.download_model()
print(f"Model saved to: {model_path}")

Complete Example

import pandas as pd
from strongly_python.mlops import automl

def main():
    # Create sample dataset
    print("Creating sample dataset...")
    df = pd.DataFrame({
        "feature1": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0],
        "feature2": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
        "feature3": [10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
        "target": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
    })

    # Create AutoML job
    print("Starting AutoML job...")
    job = automl.create_job(
        name="churn-prediction-model",
        data=df,
        target_column="target",
        problem_type="binary",
        time_limit=300,
        preset="medium_quality"
    )
    print(f"Job ID: {job.job_id}")
    print(f"Status: {job.status}")

    # Wait for completion
    print("\nWaiting for AutoML to complete...")
    job.wait()

    # Get results
    print("\n--- AutoML Results ---")
    print(f"Status: {job.status}")

    # View leaderboard
    print("\n--- Model Leaderboard ---")
    leaderboard = job.get_leaderboard()
    for i, entry in enumerate(leaderboard[:5]):
        print(f"{i+1}. {entry.model_name}: score={entry.score:.4f}")

    # Get best model
    best = job.get_best_model()
    print(f"\nBest Model: {best.model_name}")
    print(f"Score: {best.score:.4f}")

    # Download model
    print("\nDownloading model...")
    model_path = job.download_model()
    print(f"Model saved to: {model_path}")

if __name__ == "__main__":
    main()

API Reference

Job Creation

automl.create_job(
    name: str,                    # Job name
    data: pd.DataFrame = None,    # Data as DataFrame
    volume_path: str = None,      # Or path to CSV in volume
    target_column: str,           # Target column name
    problem_type: str,            # binary, multiclass, regression
    time_limit: int = 300,        # Training time in seconds
    preset: str = "medium_quality"  # Quality preset
)

Job Object

Method/Property	Description
job.job_id	Unique job identifier
job.status	Current status
job.wait()	Block until completion
job.refresh()	Update status
job.get_leaderboard()	Get ranked models
job.get_best_model()	Get best model info
job.download_model()	Download model artifact

Job Status Values

Status	Description
pending	Job queued
running	Training in progress
completed	Training finished
failed	Training failed