__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, model building utilities,
and Feature Store operations.

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 operations (FeatureGroup, ingestion, Athena queries)

Example usage:
    from sagemaker.mlops import ModelBuilder
    from sagemaker.mlops.workflow import Pipeline, TrainingStep
    from sagemaker.mlops.feature_store import FeatureGroup, ingest_dataframe
"""
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, ingest_dataframe, etc.

__all__ = [
    "ModelBuilder",
    "workflow",  # Submodule
    "feature_store",  # Submodule - Feature Store operations
]