Leveraging Observability and AI in Software Development

Overview

Discover how to effectively integrate observability and AI techniques into your software development workflow with expert insights from Matthew Bonig.

Syllabus

• Introduction to Observability and AI in Software Development
Overview of Observability
Overview of AI in Software Development
Importance of Integration
• Key Concepts in Observability
Monitoring vs. Observability
Logs, Metrics, and Traces
Tools and Platforms
• Key Concepts in Artificial Intelligence
Machine Learning Basics
AI Models for Software Development
Tools and Frameworks for AI
• Integrating Observability with AI
How Observability Enhances AI Models
Automated Insights and Anomaly Detection
Case Studies
• Implementing Observability and AI in the Development Lifecycle
AI-Driven Alerting and Monitoring
Feedback Loops and Continuous Improvement
Best Practices for Integration
• Tools and Technologies
Choosing the Right Observability Tools
AI Tooling and Platforms
Open Source Solutions
• Real-world Applications and Examples
Use Cases in Software Development
Success Stories and Insights
• Challenges and Considerations
Common Pitfalls
Ethical and Privacy Concerns
Performance Impact
• Hands-on Labs and Workshops
Setting Up Observability in a Sample Project
Implementing AI for Monitoring
Debugging and Optimizing Using Integrated Observability and AI
• Expert Insights and Future Trends
Matthew Bonig's Learnings and Experiences
Emerging Technologies and Trends
Preparing for the Future of Software Development
• Final Project and Assessment
Developing an Observability and AI-Enabled Software Application
Peer Review and Feedback Session
Course Wrap-up and Next Steps

Subjects

Programming