GIAC Machine Learning Engineer (GMLE) icon

GIAC Machine Learning Engineer (GMLE)

Practitioner Certification

The GIAC Machine Learning Engineer (GMLE) certification validates a practitioner’s knowledge of practical data science, statistics, probability, and machine learning. GMLE certification holders have demonstrated that they are qualified to solve real-world cyber security problems using Machine Learning.

Areas Covered

  • Anomaly detection and optimization
  • Convolutional neural networks
  • Data acquisition
  • Data exploration and visualization
  • Data manipulation and analysis
  • Deep learning neural networks
  • Inferential statistics and probability
  • Loss functions
  • Probability and inference
  • Python scripting
  • Supervised and unsupervised learning

Who is GMLE for?

  • Data scientists
  • Forensic analysts
  • Infosec professionals who want to understand machine learning
  • Security analysts
  • Security engineers

GMLE with CyberLive

GIAC knows that cyber security professionals need:

  • Discipline-specific certifications
  • Practical testing that validates their knowledge and hands-on skills

In response to this industry-wide need, GIAC developed CyberLive - hands-on, real-world practical testing.

CyberLive testing creates a lab environment where cyber practitioners prove their knowledge, understanding, and skill using:

  • Actual programs
  • Actual code
  • Virtual machines

Candidates are asked practical questions that require performance of real-world-like tasks that mimic specialized job roles.

Find out more about CyberLive here.

Exam Format

  • 1 proctored exam
  • 110 questions
  • 3 hours
  • Minimum passing score of 65%

Note: GIAC reserves the right to change the specifications for each certification without notice. Based on a scientific passing point study, the passing point for the GSEC exam has been determined to be 73% for all candidates receiving access to their certification attempts on or after August 6th, 2017. To verify the format and passing point of your specific certification attempt, read the Certification Information found in your account at


NOTE: All GIAC Certification exams are web-based and required to be proctored. There are two proctoring options: remote proctoring through ProctorU, and onsite proctoring through PearsonVUE. Click here for more information.

GIAC certification attempts will be activated in your GIAC account after your application has been approved and according to the terms of your purchase. Details on delivery will be provided along with your registration confirmation upon payment. You will receive an email notification when your certification attempt has been activated in your account. You will have 120 days from the date of activation to complete your certification attempt.

Exam Certification Objectives & Outcome Statements

  • Anomaly Detection and Optimization
    The candidate will demonstrate a fundamental understanding autoencoders and how they are used in anomaly detection problems. The candidate will also demonstrate a fundamental understanding of how genetic algorithms are applied to automate the optimization of neural networks.
  • Clustering
    The candidate will demonstrate a fundamental understanding of machine learning concepts such as clustering, and unsupervised machine learning.
  • Convolutional Neural Networks
    The candidate will demonstrate a fundamental understanding of how convolutional neural networks are used to solve classification problems as well as for predictive analytics.
  • Data Acquisition
    The candidate will demonstrate a fundamental understanding of data acquisition, cleaning, and manipulation terminology and the steps necessary to prepare threat data for additional threat hunting analysis. The candidate will demonstrate familiarity with accessing data from SQL, document stores, and by web scraping.
  • Leveraging Python
    The candidate will demonstrate a fundamental understanding of the Python scripting language and modules such as NumPy, Pandas, and TensorFlow and how to leverage them to extract, visualize, transform, and load data.
  • Neural Networks
    The candidate will demonstrate a fundamental understanding of deep learning concepts using neural networks for supervised machine learning. Candidates will demonstrate an understanding of loss and error functions, vectors, matrices and tensors.
  • Probability and Frequency
    The candidate will demonstrate a fundamental understanding of probability theory, inference, the Bayes theorem and Fourier series.
  • Regressions
    The candidate will demonstrate a fundamental understanding of regressions and their application in deep learning.
  • Statistics Fundamentals
    The candidate will demonstrate a fundamental understanding of statistics and how it is applied to data science for threat hunting use cases. The candidate will demonstrate familiarity with terminology such as mean, and median.
  • Supervised Learning
    The candidate will demonstrate a fundamental understanding of support vector classifiers, kernel functions, support vector machines, decision trees and random forests.

Other Resources

  • Training is available in a variety of modalities including live training and OnDemand.
  • Practical work experience can help ensure that you have mastered the skills necessary for certification.
  • College level courses or self paced study through another program or materials may meet the needs for mastery.
  • Get information about the procedure to contest exam results.

Practice Tests

  • These tests are a simulation of the real exam allowing you to become familiar with the test engine and style of questions.
  • Practice exams are a gauge to determine if your preparation methods are sufficient.
  • The practice bank questions are limited so you may encounter the same question on practice tests when multiple practice tests are purchased.
  • Practice exams never include actual exam questions.
  • Purchase a GMLE practice test here.
  • GIAC recommends leveraging additional study methods for test preparation.


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