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Starts 7 June 2025 18:24

Ends 7 June 2025

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How to Use Kubernetes to Build a Data Lake for AI Workloads

Learn to build a cloud-agnostic data lake using Kubernetes, Rook, and Ceph for AI workloads. Discover how to provide unified data access across multiple data centers for data scientists and developers.
CNCF [Cloud Native Computing Foundation] via YouTube

CNCF [Cloud Native Computing Foundation]

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Overview

Learn to build a cloud-agnostic data lake using Kubernetes, Rook, and Ceph for AI workloads. Discover how to provide unified data access across multiple data centers for data scientists and developers.

Syllabus

  • Course Introduction
  • Overview of Data Lakes and AI Workloads
    Importance of Cloud-Agnostic Solutions
    Course Structure and Objectives
  • Introduction to Kubernetes
  • Kubernetes Architecture and Components
    Key Concepts: Pods, Nodes, and Clusters
    Kubernetes Networking and Storage Basics
  • Understanding Ceph and Rook
  • Introduction to Ceph: Architecture and Components
    Rook: Orchestrating Ceph in Kubernetes
    Setting Up Rook and Ceph in a Kubernetes Environment
  • Designing and Building a Data Lake
  • Defining Requirements for AI Workloads
    Architecting a Scalable Data Lake with Kubernetes
    Leveraging Object Storage with Ceph in Kubernetes
  • Implementing Data Lake with Kubernetes
  • Deploying Rook and Ceph for Data Lake Storage
    Configuring Storage Classes and Persistent Volumes
    Ensuring Data Accessibility and Reliability
  • Data Access and Management
  • Providing Unified Data Access Across Multiple Data Centers
    Implementing Data Security and Governance
    Monitoring and Managing Data Lake Performance
  • Integration with AI Workloads
  • Connecting AI Frameworks (e.g., TensorFlow, PyTorch) to Data Lake
    Best Practices for Data Ingestion and Preprocessing
    Optimizing Data Access for AI Model Training
  • Cloud-Agnostic Data Lake Strategies
  • Ensuring Portability Across Cloud Providers
    Hybrid and Multi-Cloud Deployment Considerations
    Using Kubernetes Features for Cloud-Agnostic Operations
  • Case Studies and Examples
  • Real-World Implementations of Data Lakes for AI
    Success Stories and Lessons Learned
  • Course Conclusion
  • Recap of Key Learning Points
    Additional Resources and Next Steps
    Final Q&A and Course Feedback
  • Hands-on Projects and Assessments
  • Building a Mini Data Lake on a Local Kubernetes Cluster
    Deploying and Testing AI Workload Integration with Ceph质

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