Summary and Quiz

Congratulations! You’ve learned the fundamentals of Amazon SageMaker. Let’s review what we have learned in this chapter and then take a quiz to validate the learning.

Summary

Here’s a summary of the most important key takeaways from this chapter:

Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler brings data preparation to the masses with over 300 prebuilt transformations and a highly visual low-code canvas. It is integrated with AWS and includes tools for data quality inspection, data visualization, and feature creation. It supports automation, scalability, and deployment and, therefore, improves the efficiency of its workflows and the performance of its models.

Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a fully managed feature store that stores the features for machine learning models and supports feature reuse and collaboration. When combined with MLOps practices, feature engineering ingestion, and deployment are simplified to fast-track ML processes and enhance the reliability of any model.

Amazon SageMaker Model Monitor

Amazon SageMaker Model Monitor monitors the credibility and performance of operational ML models. It fetches new data, detects data drift and model performance degradation against statistical checkpoints, and sends corresponding alerts for timely actions to remain compliant and high-performing throughout the model’s life cycle.

SageMaker JumpStart

Amazon SageMaker JumpStart is an immensely useful resource for jump-starting machine learning projects by offering end-to-end solutions and templates built on foundational models and prebuilt algorithms. Specifically, JumpStart’s capabilities include feature engineering, fine-tuning, and secure data storage, which let the company make the AI system more ethical and deploy it promptly for business applications.

Amazon SageMaker Clarify

Amazon SageMaker Clarify enables multimodal, fair, and privacy-preserving solutions and sophisticated, intelligent models. It addresses unwanted biases and increases model interpretability by Shapley values and Partial Dependency Plots (PDPs). It offers compliance solutions for industry standards. When incorporated into the ML process, Clarify can help organizations achieve responsible AI while maintaining the model’s security and explainability.

Amazon SageMaker Model Cards

Amazon SageMaker Model Cards capture key information about a machine learning model, including its use case, performance, and potential harms, thus encouraging accountability. They enable version control, compliance, and easy sharing and can operate smoothly with other AWS products such as SageMaker Clarify and Model Monitor. They are suitable for industry sectors requiring well-developed documentation and ethical oversight.

Test your knowledge

Take a short quiz to validate your knowledge about Amazon SageMaker and to make sure you haven’t missed out on anything: