BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!
Course Overview
In this two – day, instructor led course in Washington, DC Metro, Tysons Corner, VA, Columbia, MD or Live Online, participants will learn about Artificial Intelligence and Machine Learning (AI/ML) Tools. This training course gives attendees an in-depth examination of the tools and techniques used in monitoring AI and ML models, focusing on those used in production. Participants learn how to detect and address model drift over time and monitor for data quality, privacy, and security. This course is intended for Data Science DevOps, Data Engineers, Data Scientists and ML Engineers. At the completion of this course, participants will be able to:
- Understand the importance and types of AI/ML model monitoring
- Know how to detect anomalies in model behavior
- Understand the practical applications of anomaly detection in AI/ML monitoring
Schedule
Currently, there are no public classes scheduled. Please contact a Phoenix TS Training Consultant to discuss hosting a private class at 301-258-8200.
Prerequisites
All learners are required to have:
- Understand the importance and types of AI/ML model monitoring
- Know how to detect anomalies in model behavior
- Understand the practical applications of anomaly detection in AI/ML monitoring
Course Outline
Introduction to Monitoring AI/ML Models
- Importance of monitoring AI/ML models
- Key metrics for monitoring AI/ML models
- Monitoring for model performance vs. monitoring for application performance
- Monitoring throughout the Data Science pipeline
Monitoring Data Quality
- Understanding data quality issues in AI/ML applications.
- Tools and techniques for monitoring data quality.
- How data quality issues affect model performance and strategies to manage this.
Detecting and Addressing Model Drift
- Understanding model drift
- Techniques for detecting model drift and data drift
- Tools for drift detection (e.g., AWS SageMaker Model Monitor, Seldon Alibi-Detect)
- Strategies for addressing model drift
Advanced Topics in AI/ML Monitoring
- Monitoring complex models (e.g., deep learning models)
- Monitoring at scale: big data considerations
- Continuous monitoring and automated anomaly detection
Monitoring for AI/ML Security
- Understanding adversarial attacks on AI/ML models.
- Importance of security monitoring in AI/ML.
- Tools for monitoring and mitigating adversarial attacks.
Privacy, Fairness, and Compliance Considerations
- How privacy regulations impact AI/ML monitoring.
- Tools and best practices for privacy-preserving AI/ML monitoring.
- Case studies in AI/ML privacy and compliance.
- Understanding model fairness and bias
- Tools for fairness and bias monitoring (e.g., Fairlearn, Aequitas)
- Case studies of fairness and bias monitoring
Conclusion
BONUS! Cyber Phoenix Subscription Included: All Phoenix TS students receive complimentary ninety (90) day access to the Cyber Phoenix learning platform, which hosts hundreds of expert asynchronous training courses in Cybersecurity, IT, Soft Skills, and Management and more!
Phoenix TS is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints re-garding registered sponsors may be submitted to the National Registry of CPE Sponsors through its web site: www.nasbaregistry.org
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