AWS SageMaker Platform Service

An AWS SageMaker Platform Service is a fully-managed end-to-end cloud-based machine learning platform provided by Amazon AWS.

• Context:
  • It can (typically) be a component of AWS Machine Learning.
  • It can (typically) be composed of AWS SageMaker Supporting Services:
    • a SageMaker Notebook Development Service (that uses SageMaker Instances).
    • a SageMaker Model Training Service.
    • a SageMaker Model Hosting Service.
    • a SageMaker Ground Truth Service.
  • …
• Example(s):
  • AWS SageMaker (2017-11-29).
  • …
• Counter-Example(s):
  • Azure ML Studio.
  • Google Vertex AI.
  • Salesforce Einstein.
  • H2O ML Platform.
  • Databricks ML Platform (w/ MLflow).
• See: AWS ML Service.

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References

2020

• https://aws.amazon.com/sagemaker/
  • QUOTE:

2017

• http://aws.amazon.com/blogs/aws/sagemaker/
  • QUOTE: Amazon SageMaker is a fully managed end-to-end machine learning service that enables data scientists, developers, and machine learning experts to quickly build, train, and host machine learning models at scale. This drastically accelerates all of your machine learning efforts and allows you to add machine learning to your production applications quickly.

    There are 3 main components of Amazon SageMaker:

    • Authoring: Zero-setup hosted Jupyter notebook IDEs for data exploration, cleaning, and preprocessing. You can run these on general instance types or GPU powered instances.
    • Model Training: A distributed model building, training, and validation service. You can use built-in common supervised and unsupervised learning algorithms and frameworks or create your own training with Docker containers. The training can scale to tens of instances to support faster model building. Training data is read from S3 and model artifacts are put into S3. The model artifacts are the data dependent model parameters, not the code that allows you to make inferences from your model. This separation of concerns makes it easy to deploy Amazon SageMaker trained models to other platforms like IoT devices.
    • Model Hosting: A model hosting service with HTTPs endpoints for invoking your models to get realtime inferences. These endpoints can scale to support traffic and allow you to A/B test multiple models simultaneously. Again, you can construct these endpoints using the built-in SDK or provide your own configurations with Docker images.

2017b

• https://console.aws.amazon.com/sagemaker/
  • QUOTE:
    • Fully-managed notebook instances: For training data exploration and preprocessing, Amazon SageMaker provides fully managed instances running Jupyter notebooks that include example code for common model training and hosting exercises.
    • Highly-optimized machine learning algorithms: Amazon SageMaker installs high-performance, scalable machine learning algorithms optimized for speed, scale, and accuracy, to run on extremely large training datasets.
    • One-click training: When you're ready to train in Amazon SageMaker, simply indicate the type and quantity of instances you need and initiate training with a single click.
  • Deployment without engineering effort
    • After training, SageMaker provides the model artifacts and scoring images to you for deployment to Amazon EC2 or anywhere else.

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