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MLOps
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Classroom Contents
Securing ML Workloads with Kubeflow and MLOps - Pwned By Statistics
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- 1 Introduction
- 2 Why ML
- 3 Why ML is hard
- 4 MLOps
- 5 Circle Detector
- 6 Wolf vs Husky Detector
- 7 Flaws in Federated Learning
- 8 Additional Techniques
- 9 Building a Pipeline
- 10 Extracting Your Model
- 11 Distillation Attack
- 12 Model Extraction Attack
- 13 Hidden Data Attack
- 14 Secret Memorization
- 15 Leakage Detection
- 16 Summary
- 17 Questions
- 18 AutoML
- 19 AI Models
- 20 Data Drift
- 21 Attack Systems
- 22 Differential Privacy
- 23 Threat Modeling
- 24 ML Ops
- 25 Outro
