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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]
2544 Courses
36 minutes
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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
- Introduction to Kubernetes
- Understanding Ceph and Rook
- Designing and Building a Data Lake
- Implementing Data Lake with Kubernetes
- Data Access and Management
- Integration with AI Workloads
- Cloud-Agnostic Data Lake Strategies
- Case Studies and Examples
- Course Conclusion
- Hands-on Projects and Assessments
Overview of Data Lakes and AI Workloads
Importance of Cloud-Agnostic Solutions
Course Structure and Objectives
Kubernetes Architecture and Components
Key Concepts: Pods, Nodes, and Clusters
Kubernetes Networking and Storage Basics
Introduction to Ceph: Architecture and Components
Rook: Orchestrating Ceph in Kubernetes
Setting Up Rook and Ceph in a Kubernetes Environment
Defining Requirements for AI Workloads
Architecting a Scalable Data Lake with Kubernetes
Leveraging Object Storage with Ceph in Kubernetes
Deploying Rook and Ceph for Data Lake Storage
Configuring Storage Classes and Persistent Volumes
Ensuring Data Accessibility and Reliability
Providing Unified Data Access Across Multiple Data Centers
Implementing Data Security and Governance
Monitoring and Managing Data Lake Performance
Connecting AI Frameworks (e.g., TensorFlow, PyTorch) to Data Lake
Best Practices for Data Ingestion and Preprocessing
Optimizing Data Access for AI Model Training
Ensuring Portability Across Cloud Providers
Hybrid and Multi-Cloud Deployment Considerations
Using Kubernetes Features for Cloud-Agnostic Operations
Real-World Implementations of Data Lakes for AI
Success Stories and Lessons Learned
Recap of Key Learning Points
Additional Resources and Next Steps
Final Q&A and Course Feedback
Building a Mini Data Lake on a Local Kubernetes Cluster
Deploying and Testing AI Workload Integration with Ceph质
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