# setup_vertex_ai.py
from google.cloud import aiplatform
from google.cloud import storage
import os

class VertexAISetup:
    """Initialize Vertex AI environment."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location

        # Initialize Vertex AI
        aiplatform.init(
            project=project_id,
            location=location,
            staging_bucket=f'gs://{project_id}-vertex-staging'
        )

        # Create necessary buckets
        self._create_buckets()

    def _create_buckets(self):
        """Create required storage buckets."""
        storage_client = storage.Client(project=self.project_id)

        buckets = [
            f'{self.project_id}-vertex-staging',
            f'{self.project_id}-vertex-data',
            f'{self.project_id}-vertex-models',
            f'{self.project_id}-vertex-pipelines'
        ]

        for bucket_name in buckets:
            try:
                bucket = storage_client.bucket(bucket_name)
                bucket.create(location=self.location)
                print(f"Created bucket: {bucket_name}")
            except Exception as e:
                print(f"Bucket {bucket_name} already exists or error: {e}")

    def enable_apis(self):
        """Enable required APIs."""
        apis = [
            'aiplatform.googleapis.com',
            'compute.googleapis.com',
            'storage.googleapis.com',
            'bigquery.googleapis.com',
            'cloudbuild.googleapis.com',
            'artifactregistry.googleapis.com'
        ]

        for api in apis:
            os.system(f'gcloud services enable {api} --project={self.project_id}')

        print("APIs enabled successfully")

    def create_service_account(self):
        """Create service account for Vertex AI."""
        sa_name = 'vertex-ai-sa'
        sa_email = f'{sa_name}@{self.project_id}.iam.gserviceaccount.com'

        # Create service account
        os.system(f'''
            gcloud iam service-accounts create {sa_name} \
                --display-name="Vertex AI Service Account" \
                --project={self.project_id}
        ''')

        # Grant necessary roles
        roles = [
            'roles/aiplatform.user',
            'roles/bigquery.user',
            'roles/storage.objectAdmin',
            'roles/compute.admin'
        ]

        for role in roles:
            os.system(f'''
                gcloud projects add-iam-policy-binding {self.project_id} \
                    --member="serviceAccount:{sa_email}" \
                    --role="{role}"
            ''')

        return sa_email

# automl_image_classification.py
from google.cloud import aiplatform
from google.cloud import storage
import pandas as pd

class AutoMLImageClassifier:
    """Train image classification models with AutoML."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        aiplatform.init(project=project_id, location=location)

    def prepare_dataset(self, image_folder, labels_csv):
        """Prepare dataset for AutoML training."""
        # Read labels
        df = pd.read_csv(labels_csv)

        # Create import file for Vertex AI
        import_data = []
        for _, row in df.iterrows():
            import_data.append({
                'imageGcsUri': f"gs://{self.project_id}-vertex-data/{image_folder}/{row['filename']}",
                'classificationAnnotation': {
                    'displayName': row['label']
                }
            })

        # Save import file
        import_file = 'image_classification_import.jsonl'
        with open(import_file, 'w') as f:
            for item in import_data:
                f.write(json.dumps(item) + '\n')

        # Upload to GCS
        storage_client = storage.Client()
        bucket = storage_client.bucket(f'{self.project_id}-vertex-data')
        blob = bucket.blob('imports/image_classification_import.jsonl')
        blob.upload_from_filename(import_file)

        return f"gs://{self.project_id}-vertex-data/imports/image_classification_import.jsonl"

    def create_dataset(self, display_name, gcs_source):
        """Create image dataset in Vertex AI."""
        dataset = aiplatform.ImageDataset.create(
            display_name=display_name,
            gcs_source=gcs_source,
            import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification
        )

        print(f"Created dataset: {dataset.resource_name}")
        return dataset

    def train_model(self, dataset, model_display_name, budget_hours=8):
        """Train AutoML image classification model."""
        job = aiplatform.AutoMLImageTrainingJob(
            display_name=f"{model_display_name}_training",
            prediction_type="classification",
            multi_label=False,
            model_type="CLOUD",
            budget_milli_node_hours=budget_hours * 1000,
        )

        model = job.run(
            dataset=dataset,
            model_display_name=model_display_name,
            training_fraction_split=0.8,
            validation_fraction_split=0.1,
            test_fraction_split=0.1,
        )

        print(f"Model trained: {model.resource_name}")
        return model

    def evaluate_model(self, model):
        """Evaluate model performance."""
        evaluations = model.list_model_evaluations()

        for evaluation in evaluations:
            metrics = evaluation.metrics
            print(f"Model Evaluation Metrics:")
            print(f"  Precision: {metrics.get('auPrc', 'N/A')}")
            print(f"  Recall: {metrics.get('auRoc', 'N/A')}")
            print(f"  Confidence Threshold: {metrics.get('confidenceThreshold', 'N/A')}")

            # Confusion matrix
            if 'confusionMatrix' in metrics:
                print("\nConfusion Matrix:")
                cm = metrics['confusionMatrix']
                for i, row in enumerate(cm['rows']):
                    print(f"  {cm['annotationSpecs'][i]['displayName']}: {row}")

    def deploy_model(self, model, endpoint_display_name):
        """Deploy model to endpoint."""
        endpoint = aiplatform.Endpoint.create(
            display_name=endpoint_display_name,
            project=self.project_id,
            location=self.location,
        )

        model.deploy(
            endpoint=endpoint,
            deployed_model_display_name=model.display_name,
            machine_type="n1-standard-4",
            min_replica_count=1,
            max_replica_count=3,
            accelerator_type="NVIDIA_TESLA_K80",
            accelerator_count=1,
        )

        print(f"Model deployed to endpoint: {endpoint.resource_name}")
        return endpoint

    def predict(self, endpoint, image_path):
        """Make prediction using deployed model."""
        with open(image_path, "rb") as f:
            image_bytes = f.read()

        # Encode image
        encoded_content = base64.b64encode(image_bytes).decode("utf-8")

        instance = {
            "content": encoded_content
        }

        prediction = endpoint.predict(instances=[instance])

        # Parse results
        results = []
        for i, pred in enumerate(prediction.predictions[0]['confidences']):
            results.append({
                'class': prediction.predictions[0]['displayNames'][i],
                'confidence': pred
            })

        return sorted(results, key=lambda x: x['confidence'], reverse=True)

# automl_tabular.py
from google.cloud import aiplatform
from google.cloud import bigquery
import pandas as pd

class AutoMLTabular:
    """Train tabular models with AutoML."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        self.bq_client = bigquery.Client(project=project_id)
        aiplatform.init(project=project_id, location=location)

    def prepare_bigquery_dataset(self, query, dataset_id, table_id):
        """Prepare data in BigQuery."""
        # Create dataset if not exists
        dataset_ref = f"{self.project_id}.{dataset_id}"
        try:
            self.bq_client.create_dataset(dataset_ref)
        except:
            pass

        # Run query and save results
        job_config = bigquery.QueryJobConfig(
            destination=f"{dataset_ref}.{table_id}",
            write_disposition="WRITE_TRUNCATE",
        )

        query_job = self.bq_client.query(query, job_config=job_config)
        query_job.result()

        print(f"Data prepared in BigQuery: {dataset_ref}.{table_id}")
        return f"bq://{dataset_ref}.{table_id}"

    def create_tabular_dataset(self, display_name, bq_source):
        """Create tabular dataset from BigQuery."""
        dataset = aiplatform.TabularDataset.create(
            display_name=display_name,
            bq_source=bq_source,
        )

        print(f"Created dataset: {dataset.resource_name}")
        return dataset

    def train_classification_model(self, dataset, target_column, model_display_name):
        """Train tabular classification model."""
        job = aiplatform.AutoMLTabularTrainingJob(
            display_name=f"{model_display_name}_training",
            optimization_prediction_type="classification",
            optimization_objective="minimize-log-loss",
        )

        # Define column transformations
        column_specs = {
            "numeric_column": "numeric",
            "categorical_column": "categorical",
            "text_column": "text",
            target_column: "categorical"  # target
        }

        model = job.run(
            dataset=dataset,
            target_column=target_column,
            training_fraction_split=0.8,
            validation_fraction_split=0.1,
            test_fraction_split=0.1,
            predefined_split_column_name=None,
            timestamp_split_column_name=None,
            weight_column=None,
            budget_milli_node_hours=1000,
            model_display_name=model_display_name,
            disable_early_stopping=False,
        )

        return model

    def train_regression_model(self, dataset, target_column, model_display_name):
        """Train tabular regression model."""
        job = aiplatform.AutoMLTabularTrainingJob(
            display_name=f"{model_display_name}_training",
            optimization_prediction_type="regression",
            optimization_objective="minimize-rmse",
        )

        model = job.run(
            dataset=dataset,
            target_column=target_column,
            training_fraction_split=0.8,
            validation_fraction_split=0.1,
            test_fraction_split=0.1,
            budget_milli_node_hours=1000,
            model_display_name=model_display_name,
        )

        return model

    def batch_predict(self, model, input_source, output_uri):
        """Run batch prediction."""
        batch_prediction_job = model.batch_predict(
            job_display_name=f"{model.display_name}_batch_prediction",
            instances_format="bigquery",
            predictions_format="bigquery",
            source_uri=input_source,
            destination_uri=output_uri,
            machine_type="n1-standard-4",
            max_replica_count=5,
        )

        batch_prediction_job.wait()

        print(f"Batch prediction completed: {batch_prediction_job.resource_name}")
        return batch_prediction_job

# custom_training_tensorflow.py
import tensorflow as tf
from google.cloud import aiplatform
import os

class CustomTensorFlowTrainer:
    """Custom TensorFlow model training on Vertex AI."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        aiplatform.init(project=project_id, location=location)

    def create_training_script(self):
        """Create TensorFlow training script."""
        training_script = '''
import tensorflow as tf
import argparse
import os
import json

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=32)
    parser.add_argument('--learning-rate', type=float, default=0.001)
    parser.add_argument('--model-dir', type=str, required=True)
    return parser.parse_args()

def create_model(input_shape, num_classes):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Conv2D(64, 3, activation='relu'),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Conv2D(128, 3, activation='relu'),
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    return model

def train_model():
    args = get_args()

    # Load data (example with CIFAR-10)
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
    x_train = x_train.astype('float32') / 255.0
    x_test = x_test.astype('float32') / 255.0
    y_train = tf.keras.utils.to_categorical(y_train, 10)
    y_test = tf.keras.utils.to_categorical(y_test, 10)

    # Create model
    model = create_model((32, 32, 3), 10)

    # Compile model
    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=args.learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    # Setup callbacks
    callbacks = [
        tf.keras.callbacks.ModelCheckpoint(
            os.path.join(args.model_dir, 'checkpoint-{epoch:02d}'),
            save_best_only=True
        ),
        tf.keras.callbacks.TensorBoard(
            log_dir=os.path.join(args.model_dir, 'logs')
        ),
        tf.keras.callbacks.EarlyStopping(
            patience=3,
            restore_best_weights=True
        )
    ]

    # Train model
    history = model.fit(
        x_train, y_train,
        batch_size=args.batch_size,
        epochs=args.epochs,
        validation_split=0.1,
        callbacks=callbacks
    )

    # Evaluate model
    test_loss, test_acc = model.evaluate(x_test, y_test)
    print(f'Test accuracy: {test_acc}')

    # Save model
    model.save(os.path.join(args.model_dir, 'model'))

    # Save metrics
    metrics = {
        'accuracy': float(test_acc),
        'loss': float(test_loss),
        'history': history.history
    }

    with open(os.path.join(args.model_dir, 'metrics.json'), 'w') as f:
        json.dump(metrics, f)

if __name__ == '__main__':
    train_model()
'''

        with open('trainer.py', 'w') as f:
            f.write(training_script)

        return 'trainer.py'

    def create_custom_job(self, display_name, training_script_path):
        """Create custom training job."""
        job = aiplatform.CustomJob(
            display_name=display_name,
            worker_pool_specs=[
                {
                    "machine_spec": {
                        "machine_type": "n1-standard-4",
                        "accelerator_type": "NVIDIA_TESLA_K80",
                        "accelerator_count": 1,
                    },
                    "replica_count": 1,
                    "container_spec": {
                        "image_uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:latest",
                        "command": ["python"],
                        "args": [
                            training_script_path,
                            "--epochs=20",
                            "--batch-size=64",
                            "--learning-rate=0.001",
                            "--model-dir=/gcs/model"
                        ],
                    },
                }
            ],
            staging_bucket=f"gs://{self.project_id}-vertex-staging",
        )

        return job

    def run_distributed_training(self, display_name):
        """Run distributed training across multiple machines."""
        job = aiplatform.CustomJob(
            display_name=display_name,
            worker_pool_specs=[
                # Chief worker
                {
                    "machine_spec": {
                        "machine_type": "n1-standard-8",
                        "accelerator_type": "NVIDIA_TESLA_V100",
                        "accelerator_count": 2,
                    },
                    "replica_count": 1,
                    "container_spec": {
                        "image_uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:latest",
                        "command": ["python"],
                        "args": ["trainer.py"],
                        "env": [
                            {"name": "TF_CONFIG", "value": '{"cluster": {...}, "task": {"type": "chief", "index": 0}}'}
                        ]
                    },
                },
                # Workers
                {
                    "machine_spec": {
                        "machine_type": "n1-standard-8",
                        "accelerator_type": "NVIDIA_TESLA_V100",
                        "accelerator_count": 2,
                    },
                    "replica_count": 3,
                    "container_spec": {
                        "image_uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-8:latest",
                        "command": ["python"],
                        "args": ["trainer.py"],
                        "env": [
                            {"name": "TF_CONFIG", "value": '{"cluster": {...}, "task": {"type": "worker", "index": 0}}'}
                        ]
                    },
                },
                # Parameter servers
                {
                    "machine_spec": {
                        "machine_type": "n1-standard-4",
                    },
                    "replica_count": 2,
                    "container_spec": {
                        "image_uri": "gcr.io/deeplearning-platform-release/tf2-cpu.2-8:latest",
                        "command": ["python"],
                        "args": ["trainer.py"],
                        "env": [
                            {"name": "TF_CONFIG", "value": '{"cluster": {...}, "task": {"type": "ps", "index": 0}}'}
                        ]
                    },
                },
            ],
        )

        job.run(sync=True)
        return job

# custom_container_training.py
from google.cloud import aiplatform
import docker
import os

class CustomContainerTrainer:
    """Train models using custom containers."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        self.docker_client = docker.from_env()
        aiplatform.init(project=project_id, location=location)

    def create_dockerfile(self):
        """Create Dockerfile for custom training."""
        dockerfile_content = '''
FROM pytorch/pytorch:1.11.0-cuda11.3-cudnn8-runtime

# Install additional dependencies
RUN pip install --no-cache-dir \
    google-cloud-storage \
    google-cloud-aiplatform \
    tensorboard \
    scikit-learn \
    pandas \
    numpy

# Copy training code
WORKDIR /app
COPY . /app

# Set environment variables
ENV PYTHONUNBUFFERED=1

# Run training script
ENTRYPOINT ["python", "train.py"]
'''

        with open('Dockerfile', 'w') as f:
            f.write(dockerfile_content)

        return 'Dockerfile'

    def create_training_script(self):
        """Create PyTorch training script."""
        training_script = '''
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import argparse
import os
import json
from google.cloud import storage

class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        return out

def train_model(args):
    # Set device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Using device: {device}")

    # Create dummy data for example
    X_train = torch.randn(1000, 20)
    y_train = torch.randint(0, 3, (1000,))
    X_test = torch.randn(200, 20)
    y_test = torch.randint(0, 3, (200,))

    # Create data loaders
    train_dataset = TensorDataset(X_train, y_train)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)

    # Initialize model
    model = SimpleNN(20, args.hidden_size, 3).to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)

    # Training loop
    for epoch in range(args.epochs):
        model.train()
        running_loss = 0.0

        for i, (inputs, labels) in enumerate(train_loader):
            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        avg_loss = running_loss / len(train_loader)
        print(f'Epoch [{epoch+1}/{args.epochs}], Loss: {avg_loss:.4f}')

    # Evaluate model
    model.eval()
    with torch.no_grad():
        X_test, y_test = X_test.to(device), y_test.to(device)
        outputs = model(X_test)
        _, predicted = torch.max(outputs.data, 1)
        accuracy = (predicted == y_test).sum().item() / y_test.size(0)
        print(f'Test Accuracy: {accuracy:.4f}')

    # Save model
    model_path = os.path.join(args.model_dir, 'model.pth')
    torch.save(model.state_dict(), model_path)

    # Save to GCS if specified
    if args.model_dir.startswith('gs://'):
        upload_to_gcs(model_path, args.model_dir)

    # Save metrics
    metrics = {
        'final_loss': avg_loss,
        'accuracy': accuracy,
        'epochs': args.epochs
    }

    with open('metrics.json', 'w') as f:
        json.dump(metrics, f)

def upload_to_gcs(local_path, gcs_path):
    """Upload file to Google Cloud Storage."""
    storage_client = storage.Client()
    bucket_name = gcs_path.split('/')[2]
    blob_name = '/'.join(gcs_path.split('/')[3:])

    bucket = storage_client.bucket(bucket_name)
    blob = bucket.blob(blob_name)
    blob.upload_from_filename(local_path)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=32)
    parser.add_argument('--learning-rate', type=float, default=0.001)
    parser.add_argument('--hidden-size', type=int, default=64)
    parser.add_argument('--model-dir', type=str, default='./model')

    args = parser.parse_args()
    train_model(args)
'''

        with open('train.py', 'w') as f:
            f.write(training_script)

        return 'train.py'

    def build_and_push_image(self, image_name):
        """Build and push Docker image to Artifact Registry."""
        # Build image
        image_tag = f"us-central1-docker.pkg.dev/{self.project_id}/vertex-ai/{image_name}:latest"

        print("Building Docker image...")
        self.docker_client.images.build(
            path=".",
            tag=image_tag,
            rm=True
        )

        # Push to Artifact Registry
        print("Pushing image to Artifact Registry...")
        self.docker_client.images.push(image_tag)

        return image_tag

    def submit_custom_container_job(self, display_name, image_uri):
        """Submit custom container training job."""
        job = aiplatform.CustomContainerTrainingJob(
            display_name=display_name,
            container_uri=image_uri,
            model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/pytorch-cpu.1-11:latest",
        )

        model = job.run(
            model_display_name=f"{display_name}_model",
            args=[
                "--epochs=20",
                "--batch-size=64",
                "--learning-rate=0.001",
                "--model-dir=gs://my-bucket/models"
            ],
            machine_type="n1-standard-4",
            accelerator_type="NVIDIA_TESLA_K80",
            accelerator_count=1,
        )

        return model

# vertex_pipelines.py
from kfp import dsl
from kfp.v2 import compiler
from kfp.v2.dsl import component, pipeline, Input, Output, Dataset, Model, Metrics
from google.cloud import aiplatform

@component(
    packages_to_install=["pandas", "scikit-learn"],
    base_image="python:3.9"
)
def load_data(
    data_path: str,
    output_dataset: Output[Dataset]
):
    """Load and preprocess data."""
    import pandas as pd

    # Load data
    df = pd.read_csv(data_path)

    # Basic preprocessing
    df = df.dropna()
    df = df.drop_duplicates()

    # Save processed data
    df.to_csv(output_dataset.path, index=False)

    # Log metadata
    output_dataset.metadata["num_rows"] = len(df)
    output_dataset.metadata["num_columns"] = len(df.columns)

@component(
    packages_to_install=["pandas", "scikit-learn"],
    base_image="python:3.9"
)
def train_model(
    dataset: Input[Dataset],
    model: Output[Model],
    metrics: Output[Metrics]
):
    """Train ML model."""
    import pandas as pd
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import accuracy_score, precision_score, recall_score
    import joblib

    # Load data
    df = pd.read_csv(dataset.path)

    # Prepare features and target
    X = df.drop('target', axis=1)
    y = df['target']

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    # Train model
    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    clf.fit(X_train, y_train)

    # Evaluate model
    y_pred = clf.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred, average='weighted')
    recall = recall_score(y_test, y_pred, average='weighted')

    # Save model
    joblib.dump(clf, model.path)

    # Log metrics
    metrics.log_metric("accuracy", accuracy)
    metrics.log_metric("precision", precision)
    metrics.log_metric("recall", recall)

@component(
    packages_to_install=["google-cloud-aiplatform"],
    base_image="python:3.9"
)
def deploy_model(
    model: Input[Model],
    project_id: str,
    location: str,
    endpoint_name: str
) -> str:
    """Deploy model to Vertex AI endpoint."""
    from google.cloud import aiplatform

    aiplatform.init(project=project_id, location=location)

    # Upload model
    vertex_model = aiplatform.Model.upload(
        display_name=f"{endpoint_name}_model",
        artifact_uri=model.uri,
        serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest"
    )

    # Create endpoint
    endpoint = aiplatform.Endpoint.create(display_name=endpoint_name)

    # Deploy model
    endpoint.deploy(
        model=vertex_model,
        deployed_model_display_name=vertex_model.display_name,
        machine_type="n1-standard-4",
        min_replica_count=1,
        max_replica_count=3
    )

    return endpoint.resource_name

@pipeline(
    name="ml-training-pipeline",
    description="End-to-end ML training pipeline"
)
def ml_pipeline(
    data_path: str,
    project_id: str,
    location: str = "us-central1"
):
    """Complete ML pipeline."""
    # Load and preprocess data
    data_task = load_data(data_path=data_path)

    # Train model
    train_task = train_model(dataset=data_task.outputs["output_dataset"])

    # Deploy model
    deploy_task = deploy_model(
        model=train_task.outputs["model"],
        project_id=project_id,
        location=location,
        endpoint_name="ml-pipeline-endpoint"
    )

class PipelineManager:
    """Manage Vertex AI Pipelines."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        aiplatform.init(project=project_id, location=location)

    def compile_pipeline(self, pipeline_func, output_path):
        """Compile pipeline to JSON."""
        compiler.Compiler().compile(
            pipeline_func=pipeline_func,
            package_path=output_path
        )

    def submit_pipeline(self, pipeline_path, display_name, parameters):
        """Submit pipeline for execution."""
        job = aiplatform.PipelineJob(
            display_name=display_name,
            template_path=pipeline_path,
            pipeline_root=f"gs://{self.project_id}-vertex-pipelines",
            parameter_values=parameters,
        )

        job.submit()
        return job

    def create_scheduled_pipeline(self, pipeline_path, schedule):
        """Create scheduled pipeline execution."""
        from google.cloud import scheduler_v1

        scheduler_client = scheduler_v1.CloudSchedulerClient()
        parent = f"projects/{self.project_id}/locations/{self.location}"

        job = {
            "name": f"{parent}/jobs/ml-pipeline-schedule",
            "description": "Scheduled ML pipeline execution",
            "schedule": schedule,  # e.g., "0 2 * * *" for daily at 2 AM
            "http_target": {
                "uri": f"https://{self.location}-aiplatform.googleapis.com/v1/projects/{self.project_id}/locations/{self.location}/pipelineJobs",
                "http_method": "POST",
                "headers": {
                    "Content-Type": "application/json",
                },
                "body": json.dumps({
                    "displayName": "scheduled-pipeline-run",
                    "templatePath": pipeline_path,
                    "pipelineRoot": f"gs://{self.project_id}-vertex-pipelines",
                }).encode()
            }
        }

        scheduler_client.create_job(parent=parent, job=job)

# model_deployment.py
from google.cloud import aiplatform
from google.cloud import monitoring_v3
import numpy as np

class ModelDeploymentManager:
    """Manage model deployment and serving."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        self.monitoring_client = monitoring_v3.MetricServiceClient()
        aiplatform.init(project=project_id, location=location)

    def deploy_with_traffic_split(self, models, endpoint_name, traffic_split):
        """Deploy multiple models with traffic splitting."""
        # Create endpoint
        endpoint = aiplatform.Endpoint.create(
            display_name=endpoint_name,
            description="Multi-model endpoint with traffic split"
        )

        # Deploy models with traffic split
        deployed_models = []
        for i, (model, traffic) in enumerate(zip(models, traffic_split)):
            deployed_model = model.deploy(
                endpoint=endpoint,
                deployed_model_display_name=f"{model.display_name}_v{i}",
                machine_type="n1-standard-4",
                min_replica_count=1,
                max_replica_count=5,
                traffic_percentage=traffic,
                sync=False
            )
            deployed_models.append(deployed_model)

        # Wait for all deployments
        for dm in deployed_models:
            dm.wait()

        return endpoint

    def create_batch_prediction_job(self, model, input_source, output_destination):
        """Create batch prediction job."""
        batch_prediction_job = model.batch_predict(
            job_display_name=f"{model.display_name}_batch_prediction",
            gcs_source=input_source,
            gcs_destination_prefix=output_destination,
            machine_type="n1-standard-4",
            max_replica_count=10,
            accelerator_type="NVIDIA_TESLA_K80",
            accelerator_count=1,
        )

        return batch_prediction_job

    def setup_model_monitoring(self, endpoint, model_name):
        """Setup model monitoring for drift detection."""
        # Create model monitoring job
        monitoring_job = aiplatform.ModelDeploymentMonitoringJob.create(
            display_name=f"{model_name}_monitoring",
            endpoint=endpoint,
            logging_sampling_strategy={"sample_rate": 0.1},
            predict_instance_schema_uri="gs://my-bucket/schemas/predict.yaml",
            analysis_instance_schema_uri="gs://my-bucket/schemas/analysis.yaml",
            objective_configs=[
                {
                    "deployed_model_id": model_name,
                    "objective_config": {
                        "training_dataset": {
                            "dataset": "bq://project.dataset.training_data",
                            "logging_sampling_strategy": {"sample_rate": 1.0},
                        },
                        "training_prediction_skew_detection_config": {
                            "skew_thresholds": {
                                "feature_1": {"value": 0.001},
                                "feature_2": {"value": 0.001},
                            }
                        },
                        "prediction_drift_detection_config": {
                            "drift_thresholds": {
                                "feature_1": {"value": 0.001},
                                "feature_2": {"value": 0.001},
                            }
                        }
                    }
                }
            ],
            alert_config={
                "user_emails": ["ml-team@company.com"],
                "enable_logging": True
            },
            schedule_config={
                "monitor_interval": {"seconds": 3600}  # Monitor every hour
            }
        )

        return monitoring_job

    def create_explanation_metadata(self):
        """Create explanation metadata for model interpretability."""
        explanation_metadata = {
            "inputs": {
                "feature_1": {
                    "input_tensor_name": "dense_input",
                    "encoding": "IDENTITY",
                    "modality": "numeric"
                },
                "feature_2": {
                    "input_tensor_name": "dense_input",
                    "encoding": "IDENTITY",
                    "modality": "numeric"
                }
            },
            "outputs": {
                "prediction": {
                    "output_tensor_name": "dense_2"
                }
            }
        }

        explanation_parameters = {
            "sampled_shapley_attribution": {
                "path_count": 10
            }
        }

        return explanation_metadata, explanation_parameters

    def online_predict_with_explanation(self, endpoint, instances):
        """Make predictions with explanations."""
        predictions = endpoint.predict(
            instances=instances,
            parameters={"generate_explanation": True}
        )

        # Parse results
        results = []
        for i, (pred, expl) in enumerate(zip(
            predictions.predictions,
            predictions.explanations
        )):
            results.append({
                "instance": instances[i],
                "prediction": pred,
                "feature_attributions": expl.attributions
            })

        return results

# custom_predictor.py
from google.cloud import aiplatform
from typing import Dict, List, Any
import numpy as np
import joblib

class CustomPredictor:
    """Custom prediction routine for Vertex AI."""

    def __init__(self):
        self._model = None
        self._preprocessor = None

    def load(self, artifacts_path: str):
        """Load model artifacts."""
        self._model = joblib.load(f"{artifacts_path}/model.pkl")
        self._preprocessor = joblib.load(f"{artifacts_path}/preprocessor.pkl")

    def preprocess(self, instances: List[Dict[str, Any]]) -> np.ndarray:
        """Preprocess input data."""
        # Convert to numpy array
        data = np.array([[
            instance['feature1'],
            instance['feature2'],
            instance['feature3']
        ] for instance in instances])

        # Apply preprocessing
        return self._preprocessor.transform(data)

    def predict(self, instances: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Make predictions."""
        # Preprocess
        preprocessed = self.preprocess(instances)

        # Predict
        predictions = self._model.predict(preprocessed)
        probabilities = self._model.predict_proba(preprocessed)

        # Format results
        results = []
        for pred, probs in zip(predictions, probabilities):
            results.append({
                "prediction": int(pred),
                "probabilities": probs.tolist(),
                "confidence": float(np.max(probs))
            })

        return results

    def postprocess(self, predictions: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """Postprocess predictions."""
        # Add business logic
        for pred in predictions:
            if pred['confidence'] < 0.7:
                pred['recommendation'] = 'Manual review recommended'
            else:
                pred['recommendation'] = 'Automated action approved'

        return predictions

# Deployment configuration
def deploy_custom_predictor(model_path, endpoint_name):
    """Deploy model with custom prediction routine."""
    # Package custom code
    from setuptools import setup, find_packages

    setup(
        name="custom_predictor",
        version="1.0",
        packages=find_packages(),
        install_requires=[
            "numpy",
            "scikit-learn",
            "joblib"
        ],
        python_requires=">=3.7",
    )

    # Upload model
    model = aiplatform.Model.upload(
        display_name=f"{endpoint_name}_model",
        artifact_uri=model_path,
        serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest",
        serving_container_predict_route="/predict",
        serving_container_health_route="/health",
        serving_container_environment_variables={
            "MODEL_NAME": "custom_model"
        },
        serving_container_command=["python", "-m", "custom_predictor"],
    )

    return model

# experiment_tracking.py
from google.cloud import aiplatform
import mlflow
import mlflow.tensorflow
from datetime import datetime

class ExperimentTracker:
    """Track ML experiments with Vertex AI."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        aiplatform.init(project=project_id, location=location)

        # Setup MLflow with Vertex AI
        mlflow.set_tracking_uri(f"https://{location}-aiplatform.googleapis.com")
        mlflow.set_experiment(f"{project_id}-experiments")

    def track_experiment(self, experiment_name, params, metrics, artifacts):
        """Track experiment details."""
        experiment = aiplatform.Experiment.create(
            experiment_name=experiment_name,
            description=f"Experiment run on {datetime.now()}",
        )

        with experiment.start_run(run_name=f"run-{datetime.now().strftime('%Y%m%d-%H%M%S')}") as run:
            # Log parameters
            for key, value in params.items():
                run.log_params({key: value})

            # Log metrics
            for key, value in metrics.items():
                run.log_metrics({key: value})

            # Log artifacts
            for artifact_path in artifacts:
                run.log_artifact(artifact_path)

            # Log model
            if 'model_path' in artifacts:
                run.log_model(artifacts['model_path'], "model")

        return experiment

    def compare_experiments(self, experiment_names):
        """Compare multiple experiments."""
        experiments = []
        for name in experiment_names:
            exp = aiplatform.Experiment(experiment_name=name)
            experiments.append(exp)

        # Get all runs
        all_runs = []
        for exp in experiments:
            runs = exp.list_runs()
            all_runs.extend(runs)

        # Compare metrics
        comparison = []
        for run in all_runs:
            comparison.append({
                'experiment': run.experiment_name,
                'run_name': run.name,
                'metrics': run.get_metrics(),
                'params': run.get_params()
            })

        return pd.DataFrame(comparison)

    def register_best_model(self, experiment_name, metric_name='accuracy'):
        """Register best model from experiment."""
        experiment = aiplatform.Experiment(experiment_name=experiment_name)

        # Find best run
        best_run = None
        best_metric = -float('inf')

        for run in experiment.list_runs():
            metrics = run.get_metrics()
            if metric_name in metrics and metrics[metric_name] > best_metric:
                best_metric = metrics[metric_name]
                best_run = run

        if best_run:
            # Register model
            model = aiplatform.Model.upload(
                display_name=f"{experiment_name}_best_model",
                artifact_uri=best_run.get_artifact_uri("model"),
                serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
            )

            # Add metadata
            model.update(
                labels={
                    "experiment": experiment_name,
                    "metric": metric_name,
                    "value": str(best_metric)
                }
            )

            return model

# model_registry.py
from google.cloud import aiplatform
from typing import Dict, List
import hashlib

class ModelRegistry:
    """Manage model versions and registry."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        aiplatform.init(project=project_id, location=location)

    def register_model(self, model_name, artifact_uri, framework, version=None):
        """Register new model version."""
        # Generate version if not provided
        if not version:
            version = self._generate_version(artifact_uri)

        # Upload model
        model = aiplatform.Model.upload(
            display_name=f"{model_name}_v{version}",
            artifact_uri=artifact_uri,
            serving_container_image_uri=self._get_container_image(framework),
            labels={
                "model_name": model_name,
                "version": version,
                "framework": framework,
                "stage": "staging"
            }
        )

        return model

    def promote_model(self, model_id, stage='production'):
        """Promote model to different stage."""
        model = aiplatform.Model(model_id)

        # Update labels
        labels = model.labels.copy()
        labels['stage'] = stage
        labels['promoted_at'] = datetime.now().isoformat()

        model.update(labels=labels)

        # If promoting to production, update the production endpoint
        if stage == 'production':
            self._update_production_endpoint(model)

        return model

    def get_model_lineage(self, model_name):
        """Get model lineage and versions."""
        # List all models with the name
        models = aiplatform.Model.list(
            filter=f'labels.model_name="{model_name}"',
            order_by="create_time desc"
        )

        lineage = []
        for model in models:
            lineage.append({
                'model_id': model.resource_name,
                'version': model.labels.get('version', 'unknown'),
                'stage': model.labels.get('stage', 'none'),
                'created': model.create_time,
                'metrics': self._get_model_metrics(model)
            })

        return lineage

    def _generate_version(self, artifact_uri):
        """Generate version hash from artifact."""
        return hashlib.sha256(artifact_uri.encode()).hexdigest()[:8]

    def _get_container_image(self, framework):
        """Get appropriate serving container image."""
        container_images = {
            'tensorflow': 'us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest',
            'pytorch': 'us-docker.pkg.dev/vertex-ai/prediction/pytorch-cpu.1-11:latest',
            'sklearn': 'us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.0-24:latest',
            'xgboost': 'us-docker.pkg.dev/vertex-ai/prediction/xgboost-cpu.1-6:latest',
        }

        return container_images.get(framework, container_images['sklearn'])

    def _update_production_endpoint(self, model):
        """Update production endpoint with new model."""
        # Find production endpoint
        endpoints = aiplatform.Endpoint.list(
            filter='labels.environment="production"'
        )

        if endpoints:
            endpoint = endpoints[0]

            # Undeploy old models
            for deployed_model in endpoint.list_models():
                if deployed_model.display_name != model.display_name:
                    endpoint.undeploy(deployed_model_id=deployed_model.id)

            # Deploy new model
            model.deploy(
                endpoint=endpoint,
                deployed_model_display_name=model.display_name,
                machine_type="n1-standard-4",
                min_replica_count=2,
                max_replica_count=10,
                traffic_percentage=100
            )

# bigquery_ml_integration.py
from google.cloud import bigquery
from google.cloud import aiplatform

class BigQueryMLIntegration:
    """Integrate BigQuery ML with Vertex AI."""

    def __init__(self, project_id):
        self.project_id = project_id
        self.bq_client = bigquery.Client(project=project_id)
        aiplatform.init(project=project_id)

    def export_bqml_model_to_vertex(self, bq_model_ref, vertex_model_name):
        """Export BigQuery ML model to Vertex AI."""
        # Export model from BigQuery
        export_job = self.bq_client.extract_model(
            bq_model_ref,
            f"gs://{self.project_id}-vertex-models/{vertex_model_name}",
        )

        export_job.result()  # Wait for export

        # Import to Vertex AI
        model = aiplatform.Model.upload(
            display_name=vertex_model_name,
            artifact_uri=f"gs://{self.project_id}-vertex-models/{vertex_model_name}",
            serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest",
        )

        return model

    def create_feature_engineering_pipeline(self, query, feature_table):
        """Create feature engineering pipeline."""
        # Create feature engineering query
        feature_query = f"""
        CREATE OR REPLACE TABLE `{self.project_id}.{feature_table}` AS
        {query}
        """

        # Execute query
        query_job = self.bq_client.query(feature_query)
        query_job.result()

        # Create Vertex AI dataset from BigQuery
        dataset = aiplatform.TabularDataset.create(
            display_name=f"{feature_table}_dataset",
            bq_source=f"bq://{self.project_id}.{feature_table}",
        )

        return dataset

# production_ml_pipeline.py
class ProductionMLPipeline:
    """Production-ready ML pipeline with best practices."""

    def __init__(self, project_id, location='us-central1'):
        self.project_id = project_id
        self.location = location
        self.setup_monitoring()

    def setup_monitoring(self):
        """Setup comprehensive monitoring."""
        # Create monitoring dashboard
        dashboard_config = {
            "displayName": "ML Pipeline Dashboard",
            "mosaicLayout": {
                "tiles": [
                    {
                        "widget": {
                            "title": "Model Predictions",
                            "xyChart": {
                                "dataSets": [{
                                    "timeSeriesQuery": {
                                        "timeSeriesFilter": {
                                            "filter": 'resource.type="aiplatform.googleapis.com/Endpoint"'
                                        }
                                    }
                                }]
                            }
                        }
                    },
                    {
                        "widget": {
                            "title": "Model Latency",
                            "xyChart": {
                                "dataSets": [{
                                    "timeSeriesQuery": {
                                        "timeSeriesFilter": {
                                            "filter": 'metric.type="aiplatform.googleapis.com/prediction/latency"'
                                        }
                                    }
                                }]
                            }
                        }
                    }
                ]
            }
        }

        return dashboard_config

    def implement_model_governance(self):
        """Implement model governance policies."""
        governance_policies = {
            "model_approval": {
                "required_metrics": ["accuracy", "precision", "recall"],
                "minimum_thresholds": {
                    "accuracy": 0.85,
                    "precision": 0.80,
                    "recall": 0.80
                },
                "required_tests": [
                    "bias_detection",
                    "fairness_check",
                    "performance_validation"
                ]
            },
            "deployment_gates": {
                "stages": ["dev", "staging", "production"],
                "approval_required": ["staging", "production"],
                "automated_tests": True
            },
            "monitoring_requirements": {
                "drift_detection": True,
                "performance_monitoring": True,
                "data_quality_checks": True
            }
        }

        return governance_policies

    def create_ci_cd_pipeline(self):
        """Create CI/CD pipeline for ML."""
        cloudbuild_config = """
steps:
  # Run unit tests
  - name: 'python:3.9'
    entrypoint: 'pytest'
    args: ['tests/']

  # Validate data
  - name: 'gcr.io/$PROJECT_ID/ml-validator'
    args: ['--data-path', 'gs://$PROJECT_ID-data/']

  # Train model
  - name: 'gcr.io/deeplearning-platform-release/tf2-gpu.2-8'
    args: ['python', 'train.py']

  # Evaluate model
  - name: 'gcr.io/$PROJECT_ID/ml-evaluator'
    args: ['--model-path', 'gs://$PROJECT_ID-models/']

  # Deploy if tests pass
  - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
    entrypoint: 'gcloud'
    args: [
      'ai', 'models', 'upload',
      '--region', 'us-central1',
      '--display-name', 'production-model',
      '--artifact-uri', 'gs://$PROJECT_ID-models/'
    ]

timeout: '3600s'
options:
  machineType: 'E2_HIGHCPU_8'
"""

        return cloudbuild_config