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MLOps track

ML Security

Model theft, adversarial inputs, data poisoning and ML supply chain.

6 concepts3 labs1 challenges
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Concepts

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Work through the concepts in order, or jump to what you need.

1

ML Security

Concept · Hands-on lab included

2

Adversarial Perturbations

Concept · Hands-on lab included

3

Model Poisoning Attacks

Concept · Hands-on lab included

4

Model Theft and Extraction

Concept · Hands-on lab included

5

Supply Chain Security

Concept · Hands-on lab included

6

Securing the ML Platform

Concept · Hands-on lab included

Practice

Hands-on labs

Apply what you've learned in guided, production-style environments.

Browse all labs

Craft and Counter an Attack

40 min · intermediate · Adversarial Perturbations

Detect a Poisoned Retrain

35 min · advanced · Model Poisoning Attacks

Simulate an Extraction Attack

40 min · intermediate · Model Theft and Extraction

Continue

Next modules

Keep your learning streak going with related modules.

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ML Lifecycle

The full ML lifecycle and the operational discipline MLOps adds.

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Experiment Tracking

MLflow tracking, run metadata, reproducibility and comparing experiments.

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Dataset Versioning

DVC, data lineage and immutability for training data.

DevOpsForge - MLOps · ML Security
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