feat(ml): add database schema, config parser, and DVC setup

- Initialize DVC with local storage backend (task 1.6)
- Create PostgreSQL schema for training_runs table (task 1.7)
- Add SQLAlchemy database connection setup (task 1.8)
- Create Pydantic config models for pipeline.yaml (task 2.1)
- Add migration runner for database setup
- Fix pyproject.toml package discovery config

services/ml/app/db.py

@@ -0,0 +1,106 @@
+"""
+Database connection and session management for the ML service.
+
+This module provides SQLAlchemy engine and session setup for PostgreSQL.
+Environment variables control the connection parameters.
+"""
+
+import os
+from contextlib import contextmanager
+from typing import Generator
+
+from sqlalchemy import create_engine, Column, Integer, String, DateTime, JSON
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker, Session
+from sqlalchemy.sql import func
+
+
+# Database connection configuration from environment
+DATABASE_URL = os.getenv(
+    "DATABASE_URL",
+    f"postgresql://{os.getenv('POSTGRES_USER', 'ml_user')}:"
+    f"{os.getenv('POSTGRES_PASSWORD', 'ml_password')}@"
+    f"{os.getenv('POSTGRES_HOST', 'localhost')}:"
+    f"{os.getenv('POSTGRES_PORT', '5432')}/"
+    f"{os.getenv('POSTGRES_DB', 'ml_service')}"
+)
+
+# Create SQLAlchemy engine
+engine = create_engine(
+    DATABASE_URL,
+    pool_pre_ping=True,  # Verify connections before using them
+    pool_size=5,         # Number of connections to maintain
+    max_overflow=10,     # Max connections beyond pool_size
+)
+
+# Create session factory
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+
+# Base class for declarative models
+Base = declarative_base()
+
+
+# Training runs model
+class TrainingRun(Base):
+    """Model for tracking ML training runs."""
+    
+    __tablename__ = "training_runs"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    run_id = Column(String(255), unique=True, nullable=False, index=True)
+    model_type = Column(String(100), nullable=False)
+    experiment_name = Column(String(255), nullable=False, index=True)
+    pipeline_config_hash = Column(String(64), nullable=False)
+    dataset_version = Column(String(100))
+    metrics_summary = Column(JSON)
+    status = Column(String(50), nullable=False, default="running", index=True)
+    created_at = Column(DateTime(timezone=True), server_default=func.now(), index=True)
+    completed_at = Column(DateTime(timezone=True))
+    
+    def __repr__(self):
+        return f"<TrainingRun(run_id='{self.run_id}', status='{self.status}')>"
+
+
+def init_db():
+    """
+    Initialize the database schema.
+    Creates all tables defined by Base.metadata.
+    """
+    Base.metadata.create_all(bind=engine)
+
+
+@contextmanager
+def get_db() -> Generator[Session, None, None]:
+    """
+    Context manager for database sessions.
+    
+    Usage:
+        with get_db() as db:
+            # Use db session here
+            training_run = TrainingRun(run_id="123", ...)
+            db.add(training_run)
+            db.commit()
+    
+    Yields:
+        Database session
+    """
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+
+def get_db_session() -> Session:
+    """
+    Get a database session (for dependency injection).
+    
+    Usage with FastAPI:
+        @app.get("/")
+        def endpoint(db: Session = Depends(get_db_session)):
+            # Use db here
+    
+    Returns:
+        Database session (caller must close it)
+    """
+    return SessionLocal()