Amazon Sage Maker - Detailed Review

Frequently Asked Questions about Amazon SageMaker

What tools are available in SageMaker for analytics and AI jobs?

Amazon SageMaker provides a unified, web-based environment that integrates various powerful tools for complete data and AI workflows. These tools include built-in IDEs for AI/ML development, such as Amazon SageMaker notebooks, and support for frameworks like PySpark, AWS Glue, and Amazon EMR. For version control and workflow management, you can use Git and Amazon MWAA. Additionally, SageMaker offers tools like SageMaker JumpStart, HyperPod, MLFlow, Pipelines, and Model Registry to streamline model development. The integrated SQL query editor allows for data exploration, analysis, and visualization.

How does SageMaker support data preparation and feature engineering?

SageMaker Data Wrangler is a key tool for importing, analyzing, preparing, and featurizing data. It integrates into your machine learning workflows to simplify and streamline data pre-processing and feature engineering, often requiring little to no coding. You can also customize your data prep workflow by adding your own Python scripts and transformations.

What is SageMaker Feature Store and how does it work?

SageMaker Feature Store is a fully managed platform to store, share, and manage features for machine learning models. It supports both online and offline features for real-time inference, batch inference, and training. The Feature Store manages batch and streaming feature engineering pipelines to reduce duplication in feature creation and improve model accuracy. Features can be discovered and shared across models and teams with secure access and control, even across AWS accounts.

How can I use SageMaker for automated machine learning?

SageMaker offers several tools for automated machine learning. For instance, SageMaker Autopilot allows users without machine learning knowledge to quickly build classification and regression models. Additionally, the AutoML step in SageMaker Pipelines can automatically train models, simplifying the process of finding the best model for your data.

Does SageMaker support human review of ML predictions?

Yes, Amazon SageMaker includes Amazon Augmented AI (A2I), which enables the workflows required for human review of ML predictions. A2I makes it easier to build human review systems or manage large numbers of human reviewers, removing the undifferentiated heavy lifting associated with these tasks.

How can I assess and analyze machine learning models in SageMaker?

You can use Amazon SageMaker Processing to run data processing tasks, including feature engineering, data validation, model evaluation, and model interpretation. SageMaker Clarify helps improve your machine learning models by detecting potential bias and explaining the predictions that models make. Additionally, SageMaker Processing APIs can track performance before and after code implementation.

Is AWS SageMaker a serverless platform?

While SageMaker itself is not entirely serverless, it leverages the serverless capabilities of AWS Lambda to provide a fully-managed machine learning platform. This means you can run code without the need to manage infrastructure, similar to a serverless environment.

What data labeling options are available in SageMaker?

SageMaker AI provides two data labeling offerings: Amazon SageMaker Ground Truth and Amazon SageMaker Ground Truth Plus. These options allow you to identify raw data such as images, text files, and videos, and add informative labels to create high-quality training datasets for your ML models.

What is included in the Amazon SageMaker Free Tier?

The Amazon SageMaker Free Tier offers new users two months of free usage to explore its features. This includes 250 hours of ml.t3.medium instance on Studio notebooks or notebook instances, 250 hours of ml.t3.medium instance on RStudio, 25 hours of ml.m5.4xlarge instance for Data Wrangler, and other resources such as training hours and feature store units.

How does SageMaker support collaboration among teams?

SageMaker supports collaboration through shared spaces, which consist of a shared JupyterServer application and a shared directory. All user profiles in an Amazon SageMaker AI domain have access to all shared spaces in the domain, facilitating teamwork and resource sharing.