Phantom Dependencies in AI and ML Projects - Managing Hidden Software Dependencies

Overview

Discover how phantom dependencies impact AI/ML projects and learn effective strategies for identifying, managing, and securing these hidden software dependencies in your applications.

Syllabus

- Introduction to Phantom Dependencies -- Definition and significance in AI/ML projects -- How phantom dependencies differ from explicit dependencies - Identifying Phantom Dependencies -- Tools and techniques for dependency analysis -- Case studies of hidden dependencies in AI/ML projects - Impact of Phantom Dependencies -- Performance implications -- Security vulnerabilities -- Maintenance challenges - Managing Phantom Dependencies -- Effective dependency management practices -- Version control strategies -- Using dependency managers (e.g., pip, conda, npm) - Securing Dependencies in AI/ML Applications -- Best practices for dependency vulnerability assessments -- Integrating security tools into the CI/CD pipeline -- Regular updates and patch management - Strategies for Dependency Optimization -- Minimizing dependency bloat -- Balancing performance with security and maintainability - Case Studies and Real-world Examples -- Lessons learned from well-known AI/ML project failures due to poor dependency management -- Success stories in mitigating phantom dependencies - Hands-on Workshop -- Practical exercises on identifying and resolving phantom dependencies -- Simulated project environment for experiential learning - Future Trends in Dependency Management -- Emerging tools and methodologies -- The role of AI in managing software dependencies - Conclusion and Best Practices Recap -- Key takeaways and actionable measures for effective dependency management in AI/ML projects

Taught by

Tags