### Deploy and invoke models with ModelBuilder

Source: https://sagemaker.readthedocs.io

Build, deploy, and invoke models using the simplified ModelBuilder interface.

```python
from sagemaker.serve import ModelBuilder

model_builder = ModelBuilder(
    model="your-model",
    model_path="s3://your-bucket/model-artifacts"
)

model = model_builder.build(model_name="my-model")
endpoint = model_builder.deploy(
    endpoint_name="my-endpoint",
    instance_type="ml.m5.xlarge",
    initial_instance_count=1
)
result = endpoint.invoke(
    body={"inputs": "your-input-data"},
    content_type="application/json"
)
```

--------------------------------

### Manage ML workflows with Pipeline

Source: https://sagemaker.readthedocs.io

Define and manage machine learning pipelines using the sagemaker.mlops module.

```python
from sagemaker.mlops import Pipeline, TrainingStep, ModelStep

pipeline = Pipeline(name="production-ml-pipeline")

training_step = TrainingStep(
    name="train-model",
    training_config=TrainingConfig(
        algorithm_specification={
            "training_image": "your-training-image"
        }
    )
)

pipeline.add_step(training_step)
```

--------------------------------

### Train models with ModelTrainer

Source: https://sagemaker.readthedocs.io

Use the unified ModelTrainer interface to configure and execute training jobs with specific input data channels.

```python
from sagemaker.train import ModelTrainer
from sagemaker.train.configs import InputData

trainer = ModelTrainer(
    training_image="your-training-image",
    role="your-sagemaker-role"
)

train_data = InputData(
    channel_name="training",
    data_source="s3://your-bucket/train-data"
)

training_job = trainer.train(input_data_config=[train_data])
```

--------------------------------

### Access resources with SageMaker Core

Source: https://sagemaker.readthedocs.io

Perform low-level, object-oriented resource management using the sagemaker.core module.

```python
from sagemaker.core.resources import TrainingJob

training_job = TrainingJob.create(
    training_job_name="my-training-job",
    role_arn="arn:aws:iam::123456789012:role/SageMakerRole",
    input_data_config=[{
        "channel_name": "training",
        "data_source": "s3://my-bucket/train"
    }]
)
```

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