Podcast
Questions and Answers
When deploying a machine learning model with SageMaker, what is the primary purpose of implementing machine learning governance?
When deploying a machine learning model with SageMaker, what is the primary purpose of implementing machine learning governance?
- To ensure the model is deployed as quickly as possible, bypassing rigorous testing.
- To limit access to the model to only a small group of data scientists.
- To reduce the cost of training the model by using smaller datasets.
- To maintain model performance, data quality, and adherence to intended uses throughout the model's lifecycle. (correct)
What is the function of SageMaker Model Cards in the context of machine learning governance?
What is the function of SageMaker Model Cards in the context of machine learning governance?
- To automatically retrain models when performance degrades.
- To monitor the real-time performance of a deployed model.
- To define and manage user roles and permissions within SageMaker.
- To gather and document essential model information, such as intended uses, risk ratings, and training details, in a centralized location. (correct)
What is the main benefit of using SageMaker Model Dashboard?
What is the main benefit of using SageMaker Model Dashboard?
- It provides a centralized portal for viewing, searching, and exploring all models within SageMaker, along with insights into risk ratings and quality metrics. (correct)
- It automates the process of creating model documentation.
- It allows users to bypass the need for model monitoring.
- It automatically optimizes model hyperparameters during training.
How does SageMaker Role Manager contribute to machine learning governance?
How does SageMaker Role Manager contribute to machine learning governance?
What capabilities does the SageMaker Model Dashboard offer for deployed models?
What capabilities does the SageMaker Model Dashboard offer for deployed models?
What is the primary function of SageMaker Model Monitor once a model is deployed in production?
What is the primary function of SageMaker Model Monitor once a model is deployed in production?
If SageMaker Model Monitor detects a significant drop in model quality, what is the recommended course of action?
If SageMaker Model Monitor detects a significant drop in model quality, what is the recommended course of action?
A loan model starts approving loans for individuals with incorrect credit scores six months after deployment. How can SageMaker tools address this issue?
A loan model starts approving loans for individuals with incorrect credit scores six months after deployment. How can SageMaker tools address this issue?
A machine learning team wants to establish a centralized system for managing different versions of their models, including associated metadata and approval workflows. Which SageMaker service would best suit this requirement?
A machine learning team wants to establish a centralized system for managing different versions of their models, including associated metadata and approval workflows. Which SageMaker service would best suit this requirement?
Which of the following is NOT a primary benefit of using SageMaker Pipelines for machine learning workflows?
Which of the following is NOT a primary benefit of using SageMaker Pipelines for machine learning workflows?
A data scientist is building a SageMaker Pipeline and needs to incorporate a step that automatically optimizes hyperparameters for a training job. Which step type should they use?
A data scientist is building a SageMaker Pipeline and needs to incorporate a step that automatically optimizes hyperparameters for a training job. Which step type should they use?
In a SageMaker Pipeline, which step is primarily responsible for transforming and preparing data for model training, including feature engineering?
In a SageMaker Pipeline, which step is primarily responsible for transforming and preparing data for model training, including feature engineering?
An organization wants to implement a CI/CD pipeline for their machine learning models. Which SageMaker service is designed to help automate the building, training, and deployment process?
An organization wants to implement a CI/CD pipeline for their machine learning models. Which SageMaker service is designed to help automate the building, training, and deployment process?
A model deployment pipeline requires a step to assess potential biases in the training data and model predictions. Which SageMaker Pipeline step should be incorporated?
A model deployment pipeline requires a step to assess potential biases in the training data and model predictions. Which SageMaker Pipeline step should be incorporated?
After training a model using SageMaker, which step in a SageMaker Pipeline is responsible for storing the model in the SageMaker Model Registry?
After training a model using SageMaker, which step in a SageMaker Pipeline is responsible for storing the model in the SageMaker Model Registry?
A data scientist wants to monitor a deployed model for data drift. Which SageMaker service can be configured for this purpose, and which pipeline step could integrate with it to check for drift against a baseline?
A data scientist wants to monitor a deployed model for data drift. Which SageMaker service can be configured for this purpose, and which pipeline step could integrate with it to check for drift against a baseline?
When building a SageMaker Pipeline, what is the typical order of steps for preparing data, training a model, and then optimizing its hyperparameters?
When building a SageMaker Pipeline, what is the typical order of steps for preparing data, training a model, and then optimizing its hyperparameters?
A company requires that all machine learning models undergo a formal approval process before being deployed. How can the SageMaker Model Registry facilitate this governance requirement?
A company requires that all machine learning models undergo a formal approval process before being deployed. How can the SageMaker Model Registry facilitate this governance requirement?
Flashcards
SageMaker Model Cards
SageMaker Model Cards
A tool to gather essential model information in one location.
Model Dashboard
Model Dashboard
A centralized repository to view and analyze all deployed models.
SageMaker Role Manager
SageMaker Role Manager
A tool to define permissions and roles for users in SageMaker.
Model Monitoring
Model Monitoring
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Alerts in Model Monitoring
Alerts in Model Monitoring
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Model Quality
Model Quality
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Data Quality
Data Quality
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Iterate on Model
Iterate on Model
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Model Drift
Model Drift
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Model Monitor
Model Monitor
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SageMaker Model Registry
SageMaker Model Registry
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Approval Status
Approval Status
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SageMaker Pipelines
SageMaker Pipelines
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MLOps
MLOps
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Processing Step
Processing Step
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Training Step
Training Step
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Tuning Step
Tuning Step
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QualityCheck Step
QualityCheck Step
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Study Notes
Machine Learning Governance Tools in SageMaker
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SageMaker Model Cards: Gather essential model information (intended uses, risk rating, training details) centrally.
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SageMaker Model Dashboard: Centralized repository for all machine learning models; provides insights into risk rating, model quality, and data quality. Allows tracking of deployed models. Highlights models violating quality thresholds for quick action.
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SageMaker Role Manager: Defines permissions and roles for personas (data scientists, MLOps engineers, data engineers) within SageMaker for proper governance.
Model Monitor
- Continuously or scheduled monitoring of deployed models for quality.
- Alerts for deviations from quality, bias, or expandability thresholds.
- Facilitates iterative model improvement (data fixing, retraining) based on alerts.
- Example: Detecting model drift in a loan model (incorrect credit score assignments after six months).
SageMaker Model Registry
- Centralized repository for tracking, managing, and versioning machine learning models.
- Allows for model approval by stewards/governance before registration in the registry.
- Useful for automating model deployments and sharing models within a company.
SageMaker Pipelines
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Automated workflow for building, training, and deploying machine learning models, essentially a CI/CD system for machine learning.
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Enables faster iteration, reduced errors, and repeatable mechanisms.
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Pipeline components:
- Processing: Data processing and feature engineering.
- Training: Training the model.
- Tuning: Hyperparameter tuning/optimization.
- AutoML: Automating model training.
- Model: Creating or registering SageMaker models (potentially in the Model Registry).
- ClarifyCheck: Drift checks (data, model bias, explainability).
- QualityCheck: Data or model quality checks.
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Typical pipeline order: Processing, Training, Tuning, AutoML, Model, ClarifyCheck, QualityCheck.
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Description
Overview of ML governance tools in SageMaker, including Model Cards, Model Dashboard, and Role Manager. Also covers Model Monitor for quality checks, and SageMaker Model Registry for centralized model management. Focus on monitoring deployed models and iterative improvement.
