AWS云形成快视和SageMaker是AWS提供的两个不同的服务，可以通过以下步骤使用它们：

1. AWS云形成快视：AWS云形成快视是一种数据可视化工具，可以帮助用户轻松创建交互式仪表板和报表。下面是一个使用AWS云形成快视的代码示例：

import boto3

# 创建AWS云形成快视客户端
client = boto3.client('quicksight', region_name='us-east-1')

# 创建数据集
response = client.create_data_set(
    AwsAccountId='1234567890',
    DataSetId='my-dataset-id',
    Name='My Dataset',
    PhysicalTableMap={
        'my-table-name': {
            'RelationalTable': {
                'DataSourceArn': 'arn:aws:quicksight:us-east-1:1234567890:datasource/my-datasource-id',
                'Schema': 'public',
                'Name': 'my-table-name'
            }
        }
    },
    ImportMode='DIRECT_QUERY',
    Permissions=[
        {
            'Principal': 'arn:aws:quicksight:us-east-1:1234567890:user/default/test-user',
            'Actions': ['quicksight:DescribeDataSet', 'quicksight:DescribeDataSetPermissions', 'quicksight:PassDataSet', 'quicksight:DescribeIngestion', 'quicksight:ListIngestions', 'quicksight:UpdateDataSet', 'quicksight:DeleteDataSet', 'quicksight:CreateIngestion', 'quicksight:CancelIngestion']
        }
    ],
    LogicalTableMap={
        'my-table-name': {
            'Alias': 'My Table',
            'Source': {
                'PhysicalTableId': 'my-table-name'
            }
        }
    }
)

# 创建数据集成功后，创建分析
response = client.create_analysis(
    AwsAccountId='1234567890',
    AnalysisId='my-analysis-id',
    Name='My Analysis',
    SourceEntity={
        'SourceTemplate': {
            'DataSetReferences': [
                {
                    'DataSetPlaceholder': 'placeholder',
                    'DataSetArn': 'arn:aws:quicksight:us-east-1:1234567890:dataset/my-dataset-id'
                }
            ],
            'Arn': 'arn:aws:quicksight:us-east-1:1234567890:template/my-template-id'
        }
    },
    Permissions=[
        {
            'Principal': 'arn:aws:quicksight:us-east-1:1234567890:user/default/test-user',
            'Actions': ['quicksight:DescribeAnalysis', 'quicksight:DescribeAnalysisPermissions', 'quicksight:PassAnalysis', 'quicksight:UpdateAnalysis', 'quicksight:DeleteAnalysis', 'quicksight:CreateDataSet', 'quicksight:CreateAnalysis', 'quicksight:UpdateAnalysisPermissions', 'quicksight:DeleteAnalysisPermissions']
        }
    ]
)

2. AWS SageMaker：AWS SageMaker是一种托管的机器学习服务，使用户能够轻松构建、训练和部署机器学习模型。下面是一个使用AWS SageMaker的代码示例：

import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri

# 获取SageMaker执行角色
role = get_execution_role()

# 创建SageMaker客户端
sagemaker = boto3.client('sagemaker', region_name='us-east-1')

# 定义训练数据的S3位置
train_data = 's3://my-bucket/train.csv'

# 定义SageMaker训练作业的输出路径
output_path = 's3://my-bucket/output'

# 创建训练作业
response = sagemaker.create_training_job(
    TrainingJobName='my-training-job',
    AlgorithmSpecification={
        'TrainingImage': get_image_uri(boto3.Session().region_name, 'xgboost'),
        'TrainingInputMode': 'File'
    },
    RoleArn=role,
    InputDataConfig=[
        {
            'ChannelName': 'train',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'S3Prefix',
                    'S3Uri': train_data,
                    'S3DataDistributionType': 'FullyReplicated'
                }
            },