Machine Learning | Amazon SageMaker

Question 1

What is Amazon SageMaker?

General

Amazon SageMaker | Machine Learning

Answer 1

Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models.

Question 2

What can I do with Amazon SageMaker?

General

Amazon SageMaker | Machine Learning

Answer 2

Amazon SageMaker enables developers and scientists to build machine learning models for use in intelligent, predictive apps.

Question 3

How do I get started with Amazon SageMaker?

General

Amazon SageMaker | Machine Learning

Answer 3

To get started with Amazon SageMaker, you log into the Amazon SageMaker console, launch a notebook instance with an example notebook, modify it to connect to your data sources, follow the example to build/train/validate models, and deploy the resulting model into production with just a few inputs.

Question 4

In which regions is Amazon SageMaker available?

General

Amazon SageMaker | Machine Learning

Answer 4

For a list of the supported Amazon SageMaker AWS regions, please visit the AWS Region Table for all AWS global infrastructure. Also for more information, see Regions and Endpoints in the AWS General Reference.

Question 5

Can I get a history of Amazon SageMaker API calls made on my account for security analysis and operational troubleshooting purposes?

General

Amazon SageMaker | Machine Learning

Answer 5

Yes. To receive a history of Amazon SageMaker API calls made on your account, you simply turn on AWS CloudTrail in the AWS Management Console. The following API calls in Amazon SageMaker Runtime are *not* recorded and delivered: InvokeEndpoint.

Question 6

What is the service availability of Amazon SageMaker?

General

Amazon SageMaker | Machine Learning

Answer 6

Amazon SageMaker is designed for high availability. There are no maintenance windows or scheduled downtimes. Amazon SageMaker APIs run in Amazon’s proven, high-availability data centers, with service stack replication configured across three facilities in each AWS region to provide fault tolerance in the event of a server failure or Availability Zone outage.

Question 7

What security measures does Amazon SageMaker have?

General

Amazon SageMaker | Machine Learning

Answer 7

Amazon SageMaker ensures that ML model artifacts and other system artifacts are encrypted in transit and at rest. Requests to the Amazon SageMaker API and console are made over a secure (SSL) connection. You pass AWS Identity and Access Management roles to Amazon SageMaker to provide permissions to access resources on your behalf for training and deployment. You can use encrypted S3 buckets for model artifacts and data, as well as pass a KMS key to Amazon SageMaker notebooks, training jobs, and endpoints, to encrypt the attached ML storage volume.

Question 8

How does Amazon SageMaker secure my code?

General

Amazon SageMaker | Machine Learning

Answer 8

Amazon SageMaker stores code in ML storage volumes, secured by security groups and optionally encrypted at rest.

Question 9

How am I charged for Amazon SageMaker?

General

Amazon SageMaker | Machine Learning

Answer 9

You pay for ML compute, storage, and data processing resources you use for hosting the notebook, training the model, performing predictions, and logging the outputs. Amazon SageMaker allows you to select the number and type of instance used for the hosted notebook, training, and model hosting. You only pay for what you use, as you use it; there are no minimum fees and no upfront commitments.

Question 10

What if I have my own notebook, training, or hosting environment?

Hosted Jupyter notebooks

Amazon SageMaker | Machine Learning

Answer 10

Amazon SageMaker provides a full end-to-end workflow, but you can continue to use your existing tools with Amazon SageMaker. You can easily transfer the results of each stage in and out of Amazon SageMaker as your business requirements dictate.

Question 11

What types of notebooks are supported?

Hosted Jupyter notebooks

Amazon SageMaker | Machine Learning

Answer 11

Currently, Jupyter notebooks are supported.

Question 12

How do you persist notebook files when I stop my workspace?

Hosted Jupyter notebooks

Amazon SageMaker | Machine Learning

Answer 12

You can persist your notebook files on the attached ML storage volume. The ML storage volume will be detached when the notebook instance is shut down and reattached when the notebook instance is relaunched. Items stored in memory will not be persisted.

Question 13

How do I increase the available resources in my notebook?

Hosted Jupyter notebooks

Amazon SageMaker | Machine Learning

Answer 13

You can modify the notebook instance and select a larger profile through the Amazon SageMaker console, after saving your files and data on the attached ML storage volume. The notebook instance will be restarted with greater available resources, with the same notebook files and installed libraries.

Question 14

How can I train a model from an Amazon SageMaker notebook?

Model Training

Amazon SageMaker | Machine Learning

Answer 14

After launching an example notebook, you can customize the notebook to fit your data source and schema, and execute the AWS APIs for creating a training job. The progress or completion of the training job is available through the Amazon SageMaker console or AWS APIs.

Question 15

Are there limits to the size of the dataset I can use for training?

Model Training

Amazon SageMaker | Machine Learning

Answer 15

There are no fixed limits to the size of the dataset you can use for training models with Amazon SageMaker.

Question 16

What data sources can I easily pull into Amazon SageMaker?

Model Training

Amazon SageMaker | Machine Learning

Answer 16

You can specify the Amazon S3 location of your training data as part of creating a training job.

Question 17

What algorithms does Amazon SageMaker use to generate models?

Model Hosting

Amazon SageMaker | Machine Learning

Answer 17

Amazon SageMaker includes built-in algorithms for linear regression, logistic regression, k-means clustering, principal component analysis, factorization machines, neural topic modeling, latent dirichlet allocation, gradient boosted trees, sequence2sequence, time series forecasting, word2vec, and image classification. Amazon SageMaker also provides optimized MXNet and Tensorflow containers. In addition, Amazon SageMaker supports your custom training algorithms provided through a Docker image adhering to the documented specification.

Question 18

Can I access the infrastructure that Amazon SageMaker runs on?

Model Hosting

Amazon SageMaker | Machine Learning

Answer 18

No. Amazon SageMaker operates the compute infrastructure on your behalf, allowing it to perform health checks, apply security patches, and do other routine maintenance. You can also deploy the model artifacts from training with custom inference code in your own hosting environment.

Question 19

How do I scale the size and performance of an Amazon SageMaker model once in production?

Model Hosting

Amazon SageMaker | Machine Learning

Answer 19

Amazon SageMaker hosting automatically scales to the performance needed for your application using Application Auto Scaling. In addition, you can manually change the instance number and type without incurring downtime through modifying the endpoint configuration.

Question 20

How do I monitor my Amazon SageMaker production environment?

Model Hosting

Amazon SageMaker | Machine Learning

Answer 20

Amazon SageMaker emits performance metrics to Amazon CloudWatch Metrics so you can track metrics, set alarms, and automatically react to changes in production traffic. In addition, Amazon SageMaker writes logs to Amazon Cloudwatch Logs to let you monitor and troubleshoot your production environment.

Question 21

What kinds of models can be hosted with Amazon SageMaker?

Model Hosting

Amazon SageMaker | Machine Learning

Answer 21

Amazon SageMaker can host any model that adheres to the documented specification for inference Docker images. This includes models created from Amazon SageMaker model artifacts and inference code.