Machine Learning Pipeline Integration

Guide to integrating machine learning pipelines with Buildly applications.

Overview 

Buildly supports integration of machine learning pipelines for AI-powered features. This guide covers ML workflow integration, model training, and deployment within the Buildly ecosystem.

Note

Coming Soon: This documentation is being actively developed. Check back for comprehensive ML pipeline integration guides.

Architecture 

ML Pipeline Components 

Core Components:

Data Ingestion - Collect and prepare training data
Feature Engineering - Transform raw data into features
Model Training - Train ML models with prepared data
Model Validation - Evaluate model performance
Model Deployment - Deploy models for inference
Monitoring - Track model performance in production

Buildly Integration Points:

# Example ML service integration
# Coming Soon: Full implementation examples

class MLPipelineService:
    """Service for ML pipeline operations"""

    def train_model(self, dataset_id):
        """Train a new model"""
        pass

    def predict(self, model_id, features):
        """Make predictions"""
        pass

    def evaluate(self, model_id, test_data):
        """Evaluate model performance"""
        pass

Supported Frameworks 

Popular ML Frameworks:

scikit-learn
TensorFlow
PyTorch
XGBoost
LightGBM

Coming Soon:

Detailed integration guides for each framework
Best practices for model versioning
Production deployment strategies
A/B testing frameworks

Data Management 

Dataset Handling 

# Example dataset management
# Coming Soon: Complete implementation

class Dataset(models.Model):
    name = models.CharField(max_length=255)
    version = models.CharField(max_length=50)
    file_path = models.CharField(max_length=500)
    schema = models.JSONField()
    created_at = models.DateTimeField(auto_now_add=True)

Model Training 

Training Pipeline 

Coming Soon:

Automated training workflows
Hyperparameter tuning
Cross-validation strategies
Distributed training setup

Model Registry 

Versioning and Storage 

Coming Soon:

Model versioning system
Model artifact storage
Metadata tracking
Model lifecycle management

Inference Integration 

API Endpoints 

Coming Soon:

RESTful inference endpoints
Batch prediction APIs
Real-time prediction services
Caching strategies

Monitoring and Logging 

Performance Tracking 

Coming Soon:

Model performance monitoring
Data drift detection
Prediction logging
Alert systems

Resources 

External Resources:

Video Resources:

Buildly YouTube Channel - AI and machine learning integration tutorials
OpenBuild YouTube Channel - ML pipeline development guides

Next Steps:

AI Model Deployment and Monitoring - Deploying ML models
API Development with Django REST Framework - Building ML APIs

Note

This section is under active development. Comprehensive guides for ML pipeline integration will be added soon. Check the Buildly GitHub organization for example implementations.