A comprehensive, modular, and intelligent AI/ML framework that covers the entire machine learning lifecycle from data preprocessing to model deployment.
from ai_ml_framework import PipelineCreator
# Create automated pipeline
pipeline = PipelineCreator()
model = pipeline.create_auto_pipeline(
data, target='target'
)
# Deploy with one line
api = pipeline.generate_api(model)Everything you need for end-to-end machine learning
Automatic model selection, hyperparameter optimization, and ensemble methods powered by AI recommendations.
Neural network architecture design, multi-framework support (TensorFlow, PyTorch), and advanced visualization.
Automated scikit-learn pipeline creation, version management, and comprehensive experiment tracking.
Automatic FastAPI generation, Docker deployment, Kubernetes support, and multi-cloud deployment.
Interactive dashboards, real-time monitoring, model interpretability, and comprehensive analytics.
Intelligent workflow suggestions, best practices integration, and performance optimization recommendations.
Modular design for maximum flexibility
Automated data analysis, intelligent preprocessing, and AI-powered recommendations for optimal data preparation.
Intelligent model selection, hyperparameter optimization, and ensemble creation with advanced optimization algorithms.
Neural network architecture design, multi-framework support, and advanced training visualization with real-time monitoring.
Automated pipeline creation, version control, experiment tracking, and production-ready deployment capabilities.
Automatic REST API creation, Docker deployment, Kubernetes support, and multi-cloud deployment options.
Interactive dashboards, real-time monitoring, model interpretability, and comprehensive analytics tools.
Intelligent workflow suggestions, best practices integration, and performance optimization recommendations.
Comprehensive experiment management, MLflow integration, version control, and reproducible workflows.
See how easy it is to build ML applications
from ai_ml_framework.preprocessing import AutoPreprocessor, DataAnalyzer
import pandas as pd
# Load your data
df = pd.read_csv('your_data.csv')
# Analyze dataset
analyzer = DataAnalyzer()
analysis = analyzer.analyze_dataset(df, target_column='target')
print(f"Data quality score: {analysis['data_quality_score']:.2f}")
# Get AI recommendations
recommender = PreprocessingRecommender()
recommendations = recommender.get_recommendations(df, 'target')
# Apply automated preprocessing
preprocessor = AutoPreprocessor(target_column='target')
X_processed, y_processed = preprocessor.fit_transform(df)
print(f"Features: {df.shape[1]} → {X_processed.shape[1]}")
print(f"Preprocessing steps: {len(preprocessor.get_preprocessing_steps())}")from ai_ml_framework.auto_ml import AutoMLSelector, HyperparameterOptimizer
from sklearn.model_selection import train_test_split
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Initialize AutoML
automl = AutoMLSelector(problem_type='classification')
# Auto-select and train models
models = automl.auto_select_models(X_train, y_train)
print(f"Selected {len(models)} models")
# Evaluate models
results = automl.evaluate_models(models, X_test, y_test)
best_model_name = max(results.keys(), key=lambda x: results[x]['accuracy'])
print(f"Best model: {best_model_name}")
# Hyperparameter optimization
optimizer = HyperparameterOptimizer()
optimized_model, best_params = optimizer.optimize_model(
models[best_model_name], X_train, y_train, n_trials=50
)
print(f"Optimized accuracy: {results[best_model_name]['accuracy']:.3f}")from ai_ml_framework.api import APIGenerator
import joblib
# Save your trained model
joblib.dump(best_model, 'my_model.pkl')
# Generate API
api_generator = APIGenerator('my_model.pkl', 'my_model')
app = api_generator.generate_api(
title="My ML Model API",
description="Auto-generated API for ML model",
version="1.0.0"
)
# Add features
api_generator.add_authentication(api_key='secure-key')
api_generator.enable_rate_limiting(requests_per_minute=100)
api_generator.add_monitoring_middleware()
# Generate deployment files
api_generator.generate_main_script('main.py')
api_generator.generate_requirements('requirements.txt')
api_generator.generate_dockerfile('Dockerfile')
print("API generated successfully!")
print("Run: python main.py")from ai_ml_framework.preprocessing import AutoPreprocessor
from ai_ml_framework.auto_ml import AutoMLSelector
from ai_ml_framework.pipeline import PipelineCreator
from ai_ml_framework.api import APIGenerator
from ai_ml_framework.utils import AIRecommendationsEngine
# Complete workflow
df = pd.read_csv('data.csv')
# 1. Get AI recommendations
recommender = AIRecommendationsEngine()
report = recommender.generate_comprehensive_report(df, 'target')
# 2. Preprocess data
preprocessor = AutoPreprocessor(target_column='target')
X_processed, y_processed = preprocessor.fit_transform(df)
# 3. Train models
automl = AutoMLSelector(problem_type='classification')
best_model = automl.auto_select_and_train(X_processed, y_processed)
# 4. Create pipeline
pipeline_creator = PipelineCreator()
pipeline = pipeline_creator.create_auto_pipeline(df, 'target')
# 5. Generate API
api_generator = APIGenerator('pipeline.pkl')
app = api_generator.generate_api()
print("🎉 Complete workflow finished!")See how we compare to other frameworks
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