Secure and optimize AI and ML with Cross-Cloud Network
AI Summary
Summary of Video: Securing and Optimizing AI/ML Workloads with Google Cloud’s Crosscloud Network Solutions
Presenters:
- DP Adera, Group Product Manager, Cloud Networking Team
- Web Huff
Key Topics Covered:
- Challenges in AI/ML Workloads:
- Need for reliable and cost-effective data transfer.
- High bandwidth and low latency requirements for model training.
- Constraints in GPU and TPU availability for large language model training.
- Kubernetes as the prevalent platform for AI/ML orchestration.
- Network Solutions Overview:
- Importance of high performance networking for AI workloads.
- Crosscloud network addresses data movement needs with low latency and predictive pricing.
- Google Cloud’s non-blocking data center networks enhance training efficiency.
- Use of TPUs to bridge the gap in accelerator availability.
- Upcoming Innovations (2025):
- Introduction of 400 Gig interconnect for improved data transfer speeds.
- GKE support for 65,000 nodes of GPUs and TPUs enabling large models.
- Enhanced GKE capabilities for hybrid and multicloud environments.
- RDMA-based VPCs providing high throughput and low latency.
- Inference Innovations:
- GK Inference Gateway for better load balancing and optimized GPU utilization.
- Ability to route requests across multiple regions to mitigate capacity issues.
- Introduction of AI safety and security measures integrated at the inference gateway.
Conclusion:
Google Cloud is advancing its infrastructure and services to support robust AI/ML workloads, emphasizing performance, scalability, and security for applications across various industries.