Continuous Intelligence - Keeping Your AI Application in Production

Overview

Explore adapting Continuous Delivery practices for AI applications, addressing challenges in transitioning from research to production and maintaining systems with evolving data.

Syllabus

- Introduction to Continuous Intelligence -- Definition and significance in AI -- Key differences between traditional applications and AI systems - AI Applications Lifecycle -- Overview of AI research and development -- Transition from research to production - Adapting Continuous Delivery for AI -- Principles of Continuous Delivery -- Unique challenges for AI systems -- Integrating CI/CD workflows with AI - Data Management in AI Systems -- Importance of data in AI -- Handling evolving datasets -- Versioning and monitoring data - Model Management and Deployment -- Best practices for model versioning -- Techniques for continuous model integration -- Strategies for model deployment in production - Monitoring and Maintenance of AI Applications -- Setting up monitoring systems for AI -- Anomaly detection and alerting -- Automated feedback loops - Addressing Drift in AI Systems -- Understanding concept and data drift -- Methods for detecting and mitigating drift - Tools and Technologies -- Overview of tools for continuous intelligence -- Case studies of successful implementations - Challenges and Solutions in AI Production Systems -- Common pitfalls and how to avoid them -- Real-world examples and lessons learned - Future Trends in Continuous Intelligence -- Emerging technologies and methodologies -- Evolving practices in AI and continuous delivery - Capstone Project -- Practical implementation of a continuous intelligence workflow -- Assessment and peer review of solutions

Taught by

Tags