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The State of GenAI and ML in the Cloud Native Ecosystem

Delve into the dynamic world of Generative AI (GenAI) and Machine Learning (ML) as they integrate within cloud native ecosystems. This insightful exploration uncovers the latest trends, significant challenges, and the emerging tools that are revolutionizing LLMOps/MLOps workflows. Learn about the implementation of AI guardrails and effective.
CNCF [Cloud Native Computing Foundation] via YouTube

CNCF [Cloud Native Computing Foundation]

2765 Courses


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Overview

Delve into the dynamic world of Generative AI (GenAI) and Machine Learning (ML) as they integrate within cloud native ecosystems. This insightful exploration uncovers the latest trends, significant challenges, and the emerging tools that are revolutionizing LLMOps/MLOps workflows.

Learn about the implementation of AI guardrails and effective strategies for optimizing production operations. This course is perfect for those seeking to deepen their understanding of AI and ML at the intersection of cutting-edge cloud technologies.

Join us as we navigate the evolving landscape of AI technology in cloud native environments, brought to you by YouTube.

This content falls under the categories of Artificial Intelligence Courses and Computer Science Courses, catering to innovators and technologists eager to stay at the forefront of technological advancements.

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