__init__.py

"""SageMaker MLOps package for workflow orchestration and model building.

This package provides high-level orchestration capabilities for SageMaker workflows,
including pipeline definitions, step implementations, and model building utilities.

The MLOps package sits at the top of the dependency hierarchy and can import from:
- sagemaker.core (foundation primitives)
- sagemaker.train (training functionality)
- sagemaker.serve (serving functionality)

Key components:
- workflow: Pipeline and step orchestration
- model_builder: Model building and orchestration
- feature_store: Feature Store management (FeatureGroup, FeatureStore, ingestion, etc.)

Example usage:
    from sagemaker.mlops import ModelBuilder
    from sagemaker.mlops.workflow import Pipeline, TrainingStep

    # Feature Store
    from sagemaker.mlops.feature_store import (
        FeatureGroup,
        FeatureStore,
        FeatureMetadata,
        create_dataset,
        ingest_dataframe,
        create_athena_query,
    )
"""
from __future__ import absolute_import

__version__ = "0.1.0"

# Model building
from sagemaker.serve.model_builder import ModelBuilder

# Workflow submodule is available via:
#   from sagemaker.mlops import workflow
#   from sagemaker.mlops.workflow import Pipeline, TrainingStep, etc.

# Feature Store submodule is available via:
#   from sagemaker.mlops import feature_store
#   from sagemaker.mlops.feature_store import FeatureGroup, FeatureStore, create_dataset, etc.

__all__ = [
    "ModelBuilder",
    "workflow",  # Submodule
    "feature_store",  # Submodule
]