SageMaker

Question 1

SageMaker

Answer 1

Fully managed service for developers and data scientists to build ML models

Collect and prepare data, Build and train ML models, then Deploy them and monitor prediction performance

Deploy with one click, automatic scaling, no servers to manage

Question 2

Real-Time Inference

Answer 2

SageMaker model deployment method for real-time endpoints that make one prediction at a time

Question 3

Serverless Inference

Answer 3

SageMaker model deployment method for workloads that have idle periods between traffic spikes and can tolerate cold starts

Question 4

Asynchronous Inference

Answer 4

SageMaker model deployment method for requests with large payload sizes up to 1GB, long processing times, and near real-time latency requirements

Question 5

Batch Transform

Answer 5

SageMaker model deployment method for getting predictions for an entire dataset

Question 6

Automatic Model Tuning

Answer 6

SageMaker feature that automatically chooses hyperparameter ranges, search
strategy, maximum runtime of a tuning job, and early stop condition

Define the Objective Metric to determine how this feature actually tunes your model

Saves you time and money; Helps you avoid wasting money on suboptimal configurations

Question 7

SageMaker Studio

Answer 7

SageMaker feature that offers end-to-end ML development from a unified interface

Team collaboration; deploy, tune, and debug ML models; automated workflows

Question 8

SageMaker Data Wrangler

Answer 8

SageMaker feature for preparing tabular and image data for ML that is imported from S3

Single interface for data selection,
cleansing, exploration, visualization,
and processing

Data preparation, transformation and
feature engineering; SQL supported

Data Quality tool for ensuring ML training data does not negatively impact inference accuracy

Question 9

ML Features

Answer 9

Inputs to ML models used during training and used for inference

For example, converting a rigid BirthDate value into a simple Age number

Important to have high quality features across your datasets in your company for re-use

Question 10

SageMaker Feature Store

Answer 10

SageMaker feature that ingests features from a variety of sources, which become available in SageMaker Studio

Ability to define the transformation of data into feature from within the store

Can publish directly from SageMaker Data Wrangler into this feature

Question 11

SageMaker Clarify

Answer 11

SageMaker feature for evaluating FMs on human-like factors, such as friendliness or humor

Leverage an AWS-managed team or bring your own employees for evaluation

Use built-in datasets or bring your
own dataset; offers built-in metrics and algorithms

Integrated with SageMaker Studio

Question 12

Model Explainability

Answer 12

SageMaker Clarify feature that offers set of tools to help explain how MLs make predictions

Understand model characteristics as a whole prior to deployment; debug predictions provided by the model after it’s deployed

Helps increase the trust and understanding of the model

Question 13

Bias Detection

Answer 13

SageMaker Clarify feature that offers ability to detect and explain biases in your
datasets and models

Measure bias using statistical metrics; specify input features and bias will be automatically detected

Question 14

SageMaker Ground Truth

Answer 14

SageMaker feature for Reinforcement Learning from Human Feedback

RLHF: Model review, customization and evaluation; Align model to human preferences; human feedback included in “reward” function

Human feedback for ML includes creating or evaluating your models, and data generation or annotation like data labels

Reviewers include Amazon Mechanical Turk workers, your employees, or third-party vendors

Question 15

SageMaker Model Cards

Answer 15

SageMaker feature for ML governance that allows you to document essential model information

Examples include intended uses, risk ratings, and training details

Question 16

SageMaker Model Dashboard

Answer 16

SageMaker feature that provides a centralized repository for information and insights for all models

Aggregates model-related information from several SageMaker features

View model cards, visualize workflow lineage, and track your endpoint performance

Question 17

SageMaker Role Manager

Answer 17

SageMaker feature for building and managing persona-based IAM roles for common ML needs

Implement least-privilege access for models are accessed securely by your roles

Question 18

SageMaker Model Monitor

Answer 18

SageMaker feature for monitoring the quality of your model in production, continuous or on-schedule

Alerts for deviations in the model quality: fix data & retrain model

For example, model starts giving loans to people who don’t have the correct credit score

Question 19

SageMaker Model Registry

Answer 19

SageMaker feature that provides centralized repository for tracking, managing, and version ML models

Catalog models, manage model versions, associate metadata with a model

Manage approval status of a model, automate model deployment, share models. and more

Question 20

SageMaker Pipeline

Answer 20

SageMaker feature that provides a workflow that automates the process of building, training, and deploying a ML model

Essentially a CI/CD service for ML; helps you easily build, train, test, and deploy 100s of models automatically

Iterate faster, reduce errors with no manual steps, repeatable mechanisms

Steps: Processing, Training, Tuning, AutoML, Model, ClarityCheck, QualityCheck

Question 21

SageMaker JumpStart

Answer 21

SageMaker feature that provides ML Hub to find pre-trained FMs, computer vision models, or NLP models

Large collection of models from Hugging
Face, Databricks, Meta, Stability AI, and more

Models can be fully customized for your data and use-case, and are deployed on SageMaker Directly

Pre-built ML solutions for demand
forecasting, credit rate prediction, fraud
detection and computer vision

Question 22

SageMaker Canvas

Answer 22

SageMaker feature for building ML models using a visual interface, no coding required

Access to ready-to-use models from
Bedrock or JumpStart; part of SageMaker Studio

Build your own custom model using AutoML powered by SageMaker Autopilot

Leverage Data Wrangler for data
preparation

Question 23

MLFlow

Answer 23

Open-source tool available on SageMaker which helps ML teams manage the entire ML lifecycle

Tracking Servers used to track runs and experiments; launch on SageMaker with a few clicks

Fully integrated with SageMaker and part of SageMaker Studio