model.deploy to allow for auto scale configuration

**Describe the feature you'd like**

today we deploy a model like so:

```python
model = SKLearn(
entry_point=script_path,
framework_version="0.20.0",
py_version="py3",
instance_type="ml.m5.2xlarge",
role=role,
sagemaker_session=sagemaker_session,
dependencies=[...],
)

predictor = model.deploy(
endpoint_name="some_name",
initial_instance_count=1,
instance_type="ml.m5.large",
predictor_cls=SKLearnPredictorJson,
)
```

**How would this feature be used? Please describe.**

When calling `model.deploy` it would be ideal if there was a way to set an autoscale policy (similar to how we can set `initial_instance_count`).

**Describe alternatives you've considered**

I'm still researching if I can use `SKLearn` class while also using boto3 to attach a policy.

从 model.deploy 入口点和 SKLearn 模型实现开始，然后查看 issue 中提到的 boto3 替代方案。确定受支持的 autoscaling policy 配置及其 API 形态；当调用方可以在部署模型时配置 autoscaling，且相关测试覆盖了该行为时，即视为完成。

由索引模型根据 Issue 内容生成。