我一直在使用Jupyter Notebook实例来启动训练作业(在另一个实例上),并部署端点(在另一个实例上)。我正在使用Sagemaker Tensorflow API,如下所示:
# create Tensorflow object and provide and entry point script
tf_estimator = TensorFlow(entry_point='tf-train.py', role='SageMakerRole',
train_instance_count=1, train_instance_type='ml.p2.xlarge',
framework_version='1.12', py_version='py3')
# train model on data on s3 and save model artifacts to s3
tf_estimator.fit('s3://bucket/path/to/training/data')
# deploy model on another instance using checkpoints saved on S3
predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.c5.xlarge',
endpoint_type='tensorflow-serving')
我一直通过Jupyter Notebook实例完成所有这些步骤。有哪些AWS服务可以用来摆脱Jupyter Notebook实例的依赖,并以无服务器方式自动化这些任务,进行模型的训练和部署?