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

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Subjects

Conference Talks