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AI FundamentalsAI Infrastructure and MLOps

10 items

1

Dynamic Quantization Before Inference

Code Quiz
2

Model Packaging Dependencies and requirements

Code Quiz
3

CI/CD Pipeline Stage Ordering for ML

Code Quiz
4

Canary and Shadow Deployment Routing

Code Quiz
5

Containerizing a Model with Dockerfile

Code Quiz
6

Data Drift Detection Computation

Code Quiz
7

Model Performance Monitoring in Production

Code Quiz
8

Concept Drift Monitoring Logic

Code Quiz
9

Batch vs Online Inference Pattern Selection

Code Quiz
10

GPU/TPU Acceleration and Resource Management

Slides / Video
Code QuizAdvanced

Concept Drift Monitoring Logic

Find the sign error in a concept-drift accuracy-drop check.

Codepython
def check_concept_drift(recent_accuracies, baseline_accuracy, tolerance=0.1):
    avg_recent = sum(recent_accuracies) / len(recent_accuracies)
    # Concept drift = model accuracy has dropped meaningfully
    return avg_recent - baseline_accuracy > tolerance

What is the bug in this concept drift check?