In this Specialization, you’ll learn how to plan, deploy, and optimize AI infrastructure on Google Cloud. You’ll explore how AI and high-performance computing workloads depend on the right deployment model, network design, storage architecture, and accelerator choice. You’ll compare options for GPU-accelerated clusters, including Google Compute Engine and Google Kubernetes Engine. You’ll also examine how GKE supports inference workflows through containerization, networking configurations, distributed training, GPU sharing, and model-level optimization. Across the networking and storage courses, you’ll connect each part of the AI pipeline from data ingestion to training, inference, serving, and archiving.
You’ll explore Cross-Cloud Network, Cloud Interconnect, Jumbo Frames, RDMA, Titanium offload, GKE Inference Gateway, IAM, Cloud Storage, Anywhere Cache, Dataflux Dataset, Cloud Storage FUSE, Managed Lustre, Hyperdisk ML, GPUs, and TPUs. By the end of this Specialization, you’ll be able to: Select deployment options for AI workloads using GCE, GKE, GPU clusters, and inference workflows. Design networking and storage choices for AI data ingestion, training, serving, and archiving. Compare GPUs and TPUs and apply optimization strategies for performance, efficiency, and flexibility.
