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.

Start at the model.deploy entry point and the SKLearn model implementation, then review the boto3 alternative mentioned in the issue. Determine the supported autoscaling policy configuration and its API shape; done means callers can configure autoscaling when deploying a model and the behavior is covered by the relevant tests.

Written by the indexing model from the issue text.