The State of GenAI and ML in the Cloud Native Ecosystem

Overview

Explore the current state of GenAI & ML in cloud native environments, including trends, challenges, and emerging tools for LLMOps/MLOps workflows, AI guardrails, and production optimization.

Syllabus

• Introduction to GenAI and ML in Cloud Native Ecosystems
Overview of Cloud Native Concepts
Defining GenAI and Machine Learning
• Current Trends in GenAI and ML
Adoption of GenAI in the Cloud
Innovations in Large Language Models (LLMs)
Integration with IoT and Edge Computing
• Challenges in Cloud Native GenAI and ML
Data Security and Privacy Concerns
Resource Management and Scalability
Regulatory and Compliance Issues
• Emerging Tools for LLMOps and MLOps
Overview of LLMOps and MLOps
Key Tools and Frameworks
Best Practices for Workflow Automation
• AI Guardrails in Production
Importance of AI Ethics and Responsibility
Tools and Techniques for Building AI Guardrails
Ensuring Model Interpretability and Transparency
• Production Optimization Strategies
Containerization and Orchestration
Continuous Integration and Delivery (CI/CD) in ML
Monitoring and Inference Optimization Techniques
• Case Studies and Industry Applications
Success Stories in Different Industries
Analysis of Failed Implementations and Lessons Learned
• Future Directions and Opportunities
Emerging Technologies and Innovations
Predictions for the Evolution of GenAI and ML in Cloud Natives
Strategic Planning for Future Developments
• Conclusion and Q&A Session
Summary of Key Points
Open Forum for Participant Questions

Subjects

Computer Science