GIAC Machine Learning Engineer Certification (GMLE)

AI & Cyber Defense Certification Overview

Learn to apply statistics, probability, neural networks, and Python-based data science to detect anomalies and hunt threats in real security data.

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Overview
Exam Objectives
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Who It's For
FAQs

What Is the GMLE Certification?

The GIAC Machine Learning Engineer (GMLE) certification validates a practitioner's knowledge of practical data science, statistics, probability, and machine learning. Certification holders have demonstrated they can apply machine learning to real-world cybersecurity problems, from threat hunting and security monitoring to building custom detection tools.

By the numbers

3 hrs

Exam duration

Questions

65%

Min. passing score

What GMLE Covers

The certification's objectives group into six practical domains that track directly with SEC595's own section structure.

Data Acquisition & Python Foundations

Data acquisition, cleaning, and Python scripting with NumPy, Pandas, and TensorFlow.

Statistics, Probability & Inference

Statistics fundamentals and probability and frequency, including Bayesian inference.

Clustering & Supervised Learning

Clustering, unsupervised learning, support vector machines, decision trees, and random forests.

Neural Networks & Regression

Neural network fundamentals, loss functions, and regression as applied to deep learning.

Convolutional Networks & Anomaly Detection

Convolutional neural networks and the autoencoder-based anomaly detection and optimization objective.

Functional Models & Deployment

Applies the same anomaly detection and optimization objective to genetic hyperparameter tuning and practical model deployment.

Prepare With This Course

SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals

Course Details

How SEC595 Prepares You for GMLE

SEC595 is built around the exam objectives that make up the GIAC Machine Learning Engineer (GMLE) certification:

Section 1, Data Acquisition, Cleaning, and Manipulation builds skills tested under Data Acquisition and Leveraging Python.
Section 2, Data Exploration and Statistics aligns with Statistics Fundamentals and Probability and Frequency.
Section 3, Essentials of Machine Learning: Trees, Forests, & K-Means builds skills tested under Clustering and Supervised Learning.
Section 4, Essentials of Machine Learning: Deep Learning aligns with Neural Networks and Regressions.
Section 5, Essentials of Machine Learning: Autoencoders builds skills tested under Convolutional Neural Networks and Anomaly Detection and Optimization.
Section 6, Essentials of Machine Learning: Functional Models and Deployment extends the Anomaly Detection and Optimization objective into genetic hyperparameter tuning and practical model deployment.

Across all six sections, 30 hands-on labs give you the chance to apply each skill in live Python and TensorFlow environments before you sit the exam.

Read the full GMLE certification overview

SEC595 Author

David Hoelzer

CEO at Enclave Forensics, Inc.

David Hoelzer has fundamentally advanced cybersecurity by pioneering the GIAC Security Expert (GSE) certification, leading AI-driven threat detection initiatives, and developing MAVIS, an open-source ML tool enhancing code review processes.

Who Should Pursue GMLE

Data Scientists

Security Analysts and Engineers

Forensic Analysts

Infosec Professionals Building Machine Learning Fluency

Blue Team and SOC Members Performing Custom Threat Hunting

Frequently Asked Questions

It proves you can apply practical data science, statistics, probability, and machine learning to real-world cybersecurity problems, including threat hunting, security monitoring, and building custom detection tools.

One proctored exam, 82 questions, 3 hours, with a minimum passing score of 65%.

GIAC certifications are renewed on a recurring cycle through continuing education credits and a maintenance fee. For the current renewal requirements, see GIAC’s renewal page.

Data scientists, forensic analysts, security analysts and engineers, infosec professionals building machine learning fluency, and blue team or SOC members doing custom threat hunting.

SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals, a 6-day course (36 hours self-paced) with 30 hands-on labs, authored by David Hoelzer. See the SEC595 course page.

Ready to earn your GMLE certification?

Add the GMLE exam attempt when you register for SEC595.

Already trained? Register for the exam directly through GIAC here.

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