The AI/ML Specialist Certification Exam validates your expertise and readiness to support AI initiatives across defense and federal agencies.
Interested in attending or scheduling this course in your area?
Submit Request
This AI/ML Specialist (623) certification evaluates candidates' ability to excel in the AI/ML Work Role (623). The certification assesses knowledge of foundational AI/ML principles and advanced analytics, integrating with Python-based Jupyter Notebooks for hands-on problem-solving and real-world AI applications. Candidates will demonstrate the ability to design, develop, and optimize AI models and intelligent systems to support mission-critical objectives, while upholding ethical AI principles and regulatory compliance frameworks.
This Certification Focuses On:
Foundational Concepts:
Explores essential AI/ML frameworks, key terminology, and fundamental tools such as TensorFlow to establish a strong technical foundation.
Machine Learning Techniques:
Examines key strategies in supervised, unsupervised, and reinforcement learning, emphasizing model training, hyperparameter tuning, and data preprocessing pipelines with practical exercises in Python environments.
Model Evaluation & Deployment Strategies:
Guides candidates in model performance assessment using metrics like confusion matrices, ROC curves, and F1 scores, while addressing model degradation, monitoring, and retraining strategies for deployment in edge computing and cloud-based environments.
Cybersecurity & Networking Integration:
Covers AI-driven threat detection techniques and highlights how machine learning models are vulnerable to adversarial attacks, emphasizing the integration of zero-trust architecture principles and secure AI pipelines.
AI Ethics & Regulations:
Addresses the ethical implications of algorithmic decision-making, data bias mitigation, and compliance with emerging standards such as EU AI Act and NIST AI Risk Management Framework.
Future AI Trends and Innovations:
Explores advancements in federated learning, large language models (LLMs), autonomous systems, and emerging transformer architectures, preparing candidates for the future AI landscape.
Interactive and Hands-On Learning:
Candidates will engage in interactive threat scenarios, real-world AI problem-solving exercises, and expert-led instruction delivered via Python Notebooks, fostering a hands-on learning environment.
Certified candidates will:
- Exhibit a foundational understanding of AI/ML concepts, tools, and techniques.
- Apply cutting-edge machine learning models effectively to solve real-world challenges.
- Demonstrate ethical awareness and adherence to AI regulations.
- Identify and mitigate risks associated with adversarial AI, model drift, and cyber vulnerabilities
The successful candidate will possess the technical knowledge and hands-on skills necessary to design, deploy, and secure AI solutions, making them invaluable contributors to mission-focused objectives and private-sector innovation.
