Getting Started Guide
Complete guide to installing, configuring, and getting started with the AI/ML Framework. Learn how to set up your environment and build your first ML pipeline.
Installation
Get the AI/ML Framework installed and running on your system with these simple steps.
System Requirements
- Python 3.8 or higher
- 8GB RAM recommended
- 2GB disk space
- GPU optional (for deep learning)
Verify Installation
python
from ai_ml_framework.preprocessing import DataAnalyzer
print("AI/ML Framework installed successfully!")Quick Start
Get up and running with a complete ML workflow in just a few minutes.
1
Load Your Data
python
import pandas as pd
from ai_ml_framework.preprocessing import DataAnalyzer
# Load your dataset
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}")
print(f"Missing values: {analysis['missing_values_summary']}")2
Preprocess Data
python
from ai_ml_framework.preprocessing import AutoPreprocessor
# Auto-preprocess data
preprocessor = AutoPreprocessor(target_column='target')
X_processed, y_processed = preprocessor.fit_transform(df)3
Train Model
python
from ai_ml_framework.auto_ml import AutoMLSelector
# Auto-select and train best model
automl = AutoMLSelector(problem_type='classification')
best_model = automl.auto_select_and_train(X_processed, y_processed)
print(f"Best model: {best_model.__class__.__name__}")4
Create Pipeline
python
from ai_ml_framework.pipeline import PipelineCreator
# Create automated pipeline
creator = PipelineCreator()
pipeline = creator.create_auto_pipeline(df, target_column='target')
# Use pipeline for predictions
predictions = pipeline.predict(df.drop('target', axis=1))
print(f"Predictions: {predictions[:5]}")5
Generate API
python
from ai_ml_framework.api import APIGenerator
# Generate REST API
api_generator = APIGenerator('pipeline.pkl')
app = api_generator.generate_api()
api_generator.generate_main_script('api_main.py')
api_generator.generate_requirements('api_requirements.txt')
print("🎉 API generated successfully!")Complete!
You've successfully created a complete ML workflow from data to API deployment!
Configuration
Customize the framework to fit your specific needs with flexible configuration options.
Basic Configuration
python
from ai_ml_framework import Config
# Basic configuration
config = Config({
'random_state': 42,
'n_jobs': -1,
'verbose': True
})- Random state for reproducibility
- Parallel processing settings
- Verbosity control
Data Configuration
python
# Data processing configuration
data_config = {
'max_categories': 100,
'missing_threshold': 0.5,
'outlier_method': 'iqr',
'scaling_method': 'standard'
}- Category handling limits
- Missing value thresholds
- Outlier detection methods
- Feature scaling options
AutoML Configuration
python
# AutoML configuration
automl_config = {
'cv_folds': 5,
'n_trials': 100,
'timeout': 300,
'metric': 'accuracy',
'models': ['rf', 'xgb', 'lgb']
}- Cross-validation settings
- Optimization parameters
- Performance metrics
- Model selection
Deployment Configuration
python
# Deployment configuration
deploy_config = {
'host': '0.0.0.0',
'port': 8000,
'workers': 4,
'reload': False,
'log_level': 'info'
}- Server settings
- Performance options
- Logging configuration
- Security settings
Environment Variables
bash
# Set environment variables
export AI_ML_FRAMEWORK_CONFIG=/path/to/config.yaml
export AI_ML_FRAMEWORK_LOG_LEVEL=INFO
export AI_ML_FRAMEWORK_CACHE_DIR=/tmp/ai_ml_cacheConfiguration File
yaml
# config.yaml
framework:
random_state: 42
n_jobs: -1
verbose: true
preprocessing:
max_categories: 100
missing_threshold: 0.5
scaling_method: "standard"
automl:
cv_folds: 5
n_trials: 100
metric: "accuracy"
deployment:
host: "0.0.0.0"
port: 8000
workers: 4Next Steps
Now that you're set up, explore these resources to get the most out of the framework.