ADVANCED Objective assessment
AWS ML Engineer Associate 4.1
Monitor Model Performance and Data
Quality latest updated version
already graded A+
Course description
In this course, you will learn techniques for monitoring and maintaining the
performance and reliability of your machine learning (ML) solutions using the
monitoring capabilities of Amazon SageMaker. You begin by establishing the
importance of monitoring and the types of drift in ML. Then, you will discover
methods to detect data drift, model quality issues, statistical bias, and
feature attribution drift. You will explore SageMaker Model Monitor for
continuous monitoring, SageMaker Clarify for detecting bias and providing
interpretable explanations, and SageMaker Model Dashboard for visualizing
and analyzing performance metrics.
This course shares best practices to help you build and maintain reliable,
high-performing, and trustworthy ML solutions that align with the AWS Well-
Architected Machine Learning Lens design principles. You will learn
approaches for proactive decision-making, automated remediation,
notifications, and retraining workflows, which will help keep your ML
solutions effective over time.
Course level: Advanced
Duration: 2 hours and 30 minutes
Activities
Online materials
Exercises
Knowledge check questions
Course objectives
Describe the AWS Well-Architected Machine Learning Lens design
principles for monitoring.
Identify best practices to monitor data quality and model performance.
, Use SageMaker Model Monitor to continuously monitor models in
production for data drift and model quality issues.
Explain how Amazon SageMaker Clarify can detect model bias and
provide interpretable explanations.
Describe the benefits and use cases of SageMaker Clarify for
attribution monitoring.
Describe the benefits of monitoring model performance in production
using A/B testing.
Explain the key features and common use cases of SageMaker Model
Dashboard.
Proactively identify issues by monitoring ML solutions and
implementing automated remediation, notifications, and retraining
workflows.
1. Monitor End-to-End ML Pipelines
Design Principle:
Comprehensive Visibility: Track metrics across all stages of the ML
lifecycle (data ingestion, preprocessing, training, deployment, and
inference).
Key Actions:
o Use Amazon CloudWatch to monitor infrastructure metrics
(CPU, memory, latency, error rates).
o Track data pipeline health with AWS Step
Functions or Amazon SageMaker Pipelines.
o Log metadata (e.g., dataset versions, hyperparameters) for
reproducibility.
2. Track Model Performance in Production
Design Principle:
Model Quality Monitoring: Detect model degradation (e.g., accuracy
drops, drift) in real time.
Key Actions:
o Use Amazon SageMaker Model Monitor to detect data drift,
bias, and concept drift.
, o Define custom metrics (e.g., precision, recall) using SageMaker
Endpoint CloudWatch Metrics.
o Set alarms for performance thresholds (e.g., ModelAccuracy <
90%).
3. Ensure Data Quality and Consistency
Design Principle:
Data Health Checks: Validate input data quality to prevent garbage-
in/garbage-out scenarios.
Key Actions:
o Use Amazon Deequ for automated data validation (e.g.,
schema checks, null values).
o Monitor feature distributions for drift using SageMaker Clarify.
o Log data lineage for traceability.
4. Monitor Infrastructure Health
Design Principle:
Resource Optimization: Ensure compute/storage resources are
scaled and utilized efficiently.
Key Actions:
o Track SageMaker endpoint metrics
(e.g., Invocations, ModelLatency, CPUUtilization).
o Use AWS Auto Scaling for inference endpoints to handle traffic
spikes.
o Monitor GPU/CPU usage for training jobs to avoid bottlenecks.
5. Enable Security and Compliance Monitoring
Design Principle:
Proactive Threat Detection: Identify unauthorized access or data
leaks.
, Key Actions:
o Use AWS CloudTrail to audit API calls (e.g., SageMaker, S3).
o Enable Amazon GuardDuty for anomaly detection in IAM roles
or data access patterns.
o Encrypt sensitive data and monitor KMS key usage.
6. Automate Alerts and Remediation
Design Principle:
Automated Responses: Reduce manual intervention for common
issues.
Key Actions:
o Create CloudWatch Alarms for critical metrics (e.g., high error
rates, low credit balances).
o Trigger AWS Lambda functions to auto-retrain models on drift
detection.
o Use Amazon EventBridge to orchestrate remediation
workflows.
7. Optimize Costs Proactively
Design Principle:
Cost-Aware Monitoring: Track and optimize ML-related expenses.
Key Actions:
o Use AWS Cost Explorer to analyze SageMaker, EC2, and S3
costs.
o Monitor idle resources (e.g., unused SageMaker endpoints).
o Use spot instances for training and auto-terminate unused
resources.
8. Ensure Traceability and Auditability
Design Principle:
AWS ML Engineer Associate 4.1
Monitor Model Performance and Data
Quality latest updated version
already graded A+
Course description
In this course, you will learn techniques for monitoring and maintaining the
performance and reliability of your machine learning (ML) solutions using the
monitoring capabilities of Amazon SageMaker. You begin by establishing the
importance of monitoring and the types of drift in ML. Then, you will discover
methods to detect data drift, model quality issues, statistical bias, and
feature attribution drift. You will explore SageMaker Model Monitor for
continuous monitoring, SageMaker Clarify for detecting bias and providing
interpretable explanations, and SageMaker Model Dashboard for visualizing
and analyzing performance metrics.
This course shares best practices to help you build and maintain reliable,
high-performing, and trustworthy ML solutions that align with the AWS Well-
Architected Machine Learning Lens design principles. You will learn
approaches for proactive decision-making, automated remediation,
notifications, and retraining workflows, which will help keep your ML
solutions effective over time.
Course level: Advanced
Duration: 2 hours and 30 minutes
Activities
Online materials
Exercises
Knowledge check questions
Course objectives
Describe the AWS Well-Architected Machine Learning Lens design
principles for monitoring.
Identify best practices to monitor data quality and model performance.
, Use SageMaker Model Monitor to continuously monitor models in
production for data drift and model quality issues.
Explain how Amazon SageMaker Clarify can detect model bias and
provide interpretable explanations.
Describe the benefits and use cases of SageMaker Clarify for
attribution monitoring.
Describe the benefits of monitoring model performance in production
using A/B testing.
Explain the key features and common use cases of SageMaker Model
Dashboard.
Proactively identify issues by monitoring ML solutions and
implementing automated remediation, notifications, and retraining
workflows.
1. Monitor End-to-End ML Pipelines
Design Principle:
Comprehensive Visibility: Track metrics across all stages of the ML
lifecycle (data ingestion, preprocessing, training, deployment, and
inference).
Key Actions:
o Use Amazon CloudWatch to monitor infrastructure metrics
(CPU, memory, latency, error rates).
o Track data pipeline health with AWS Step
Functions or Amazon SageMaker Pipelines.
o Log metadata (e.g., dataset versions, hyperparameters) for
reproducibility.
2. Track Model Performance in Production
Design Principle:
Model Quality Monitoring: Detect model degradation (e.g., accuracy
drops, drift) in real time.
Key Actions:
o Use Amazon SageMaker Model Monitor to detect data drift,
bias, and concept drift.
, o Define custom metrics (e.g., precision, recall) using SageMaker
Endpoint CloudWatch Metrics.
o Set alarms for performance thresholds (e.g., ModelAccuracy <
90%).
3. Ensure Data Quality and Consistency
Design Principle:
Data Health Checks: Validate input data quality to prevent garbage-
in/garbage-out scenarios.
Key Actions:
o Use Amazon Deequ for automated data validation (e.g.,
schema checks, null values).
o Monitor feature distributions for drift using SageMaker Clarify.
o Log data lineage for traceability.
4. Monitor Infrastructure Health
Design Principle:
Resource Optimization: Ensure compute/storage resources are
scaled and utilized efficiently.
Key Actions:
o Track SageMaker endpoint metrics
(e.g., Invocations, ModelLatency, CPUUtilization).
o Use AWS Auto Scaling for inference endpoints to handle traffic
spikes.
o Monitor GPU/CPU usage for training jobs to avoid bottlenecks.
5. Enable Security and Compliance Monitoring
Design Principle:
Proactive Threat Detection: Identify unauthorized access or data
leaks.
, Key Actions:
o Use AWS CloudTrail to audit API calls (e.g., SageMaker, S3).
o Enable Amazon GuardDuty for anomaly detection in IAM roles
or data access patterns.
o Encrypt sensitive data and monitor KMS key usage.
6. Automate Alerts and Remediation
Design Principle:
Automated Responses: Reduce manual intervention for common
issues.
Key Actions:
o Create CloudWatch Alarms for critical metrics (e.g., high error
rates, low credit balances).
o Trigger AWS Lambda functions to auto-retrain models on drift
detection.
o Use Amazon EventBridge to orchestrate remediation
workflows.
7. Optimize Costs Proactively
Design Principle:
Cost-Aware Monitoring: Track and optimize ML-related expenses.
Key Actions:
o Use AWS Cost Explorer to analyze SageMaker, EC2, and S3
costs.
o Monitor idle resources (e.g., unused SageMaker endpoints).
o Use spot instances for training and auto-terminate unused
resources.
8. Ensure Traceability and Auditability
Design Principle:
