.. _ml-pipelines:

Machine Learning Pipeline Integration
======================================

Guide to integrating machine learning pipelines with Buildly applications.

.. contents:: Table of Contents
   :local:
   :depth: 2

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:**

.. code-block:: python

   # 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
~~~~~~~~~~~~~~~~

.. code-block:: python

   # 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:**

- `scikit-learn Documentation <https://scikit-learn.org/>`_
- `TensorFlow Guide <https://www.tensorflow.org/>`_
- `MLflow <https://mlflow.org/>`_

**Video Resources:**

- `Buildly YouTube Channel <https://www.youtube.com/@buildlyio>`_ - AI and machine learning integration tutorials
- `OpenBuild YouTube Channel <https://www.youtube.com/@openbuild7609>`_ - ML pipeline development guides

**Next Steps:**

- :doc:`/ai/model-deployment` - Deploying ML models
- :doc:`/backend/api-development` - 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.