Amazon Machine Learning Algorithms and SageMaker Algorithms

Question

1) According to http://docs.aws.amazon.com/machine-learning/latest/dg/learning-algorithm.html Amazon ML uses SGD. However, I can’t find how many hidden layers are used in the neural network?

2) Can anyone confirm that SageMaker can do what Amazon ML does? those. Is SageMaker More Powerful Than Amazon ML?

Answer 1

I'm not sure about Amazon ML, but SageMaker uses the docker containers listed here for built-in training: https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html

So, in general, everything you can do with Amazon ML you should be able to do with SageMaker (although Amazon ML has a pretty sweet schematic editor).

You can test each of these containers to dive deep into how it all works.

Here you can find an exhaustive list of available algorithms in SageMaker: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html

So far, as of December 2017, all of these algorithms are available:

• Line student
• Factor machines
• XGBoost Algorithm
• Image Classification Algorithm
• Amazon SageMaker Sequence2Sequence
• K-Means Algorithm
• Principal Component Analysis (PCA)
• Hidden Dirichlet Distribution (LDA)
• Neural Topic Model (NTM)

The general SageMaker SDK interface for these algorithms looks something like this:

from sagemaker import KMeans kmeans = KMeans(role="SageMakerRole", train_instance_count=2, train_instance_type='ml.c4.8xlarge', data_location="s3://training_data/", output_path="s3://model_artifacts/", k=10) 

The libraries here: https://github.com/awslabs/amazon-sagemaker-examples and here: https://github.com/aws/sagemaker-python-sdk are especially useful for playing with SageMaker.

You can also use Spark with SageMaker the Spark library here: https://github.com/aws/sagemaker-spark