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☁️Infrastructure
Cloud AI Architect
Cloud Infrastructure & MLOps for Production AI
The definitive program for engineers who want to own the infrastructure layer of AI systems. You will architect cloud-native AI platforms, build CI/CD pipelines for LLMs and ML models, implement observability stacks for AI workloads, and master the operational excellence required to run AI in production at scale. Hands-on with Vertex AI, SageMaker, Kubeflow, Terraform, Prometheus/Grafana, and the full MLOps toolchain.
Duration
4 months
Level
Intermediate → Advanced
Starts
October 1, 2026
Modules
4 modules
What You'll Learn
Cloud-native AI platform design (GCP/AWS/Azure)
Kubeflow & Vertex AI Pipelines for ML orchestration
LLM inference infrastructure: vLLM, TGI, Triton at scale
MLOps CI/CD: automated train → evaluate → deploy → monitor
AIOps: Prometheus, Grafana, OpenTelemetry, drift detection
GPU cost optimization: spot instances, autoscaling, batching
Infrastructure as Code: Terraform & Pulumi for ML platforms
Multi-cloud and hybrid AI deployment strategies
Full Curriculum
Detailed Syllabus
01
Weeks 1–3
Cloud Foundations for AI Workloads
- Cloud-native architecture patterns: microservices, event-driven, serverless for AI
- Compute deep-dive: GPU instance families (A100/H100/L4), TPUs, serverless inference (Cloud Run, Lambda)
- Storage architecture: object stores (GCS/S3), vector databases, feature stores (Feast, Vertex Feature Store)
- Networking: VPC design for distributed training, private endpoints, low-latency inference topologies
- IAM and security: least-privilege service accounts, Workload Identity, secrets management (Secret Manager, Vault)
- FinOps for AI: committed use discounts, spot/preemptible instances, cost allocation tagging
- Lab: Architect and provision a secure, multi-tier AI platform on GCP with Terraform
02
Weeks 4–7
MLOps Pipelines & CI/CD for Models
- ML pipeline orchestration: Kubeflow Pipelines, Vertex AI Pipelines, Apache Airflow — comparison and when to use each
- Model versioning and artifact management: MLflow Model Registry, Vertex AI Model Registry
- Feature stores: Feast architecture, Vertex AI Feature Store, offline vs online serving
- Automated training pipelines: data validation (Great Expectations, TFDV), training, evaluation, gating
- CI/CD for ML models: GitHub Actions, Cloud Build — trigger-based retraining, model gating
- Deployment patterns: blue-green, canary, shadow deployments for models
- A/B testing infrastructure for models: traffic splitting, metric collection, auto-promotion
- Drift detection and automated retraining triggers: data drift (Evidently AI), concept drift
- Lab: Build a fully automated MLOps pipeline: Git commit → retrain → evaluate → canary deploy on Vertex AI
03
Weeks 8–11
LLM Inference Infrastructure & AIOps
- LLM serving deep-dive: vLLM (PagedAttention, continuous batching), Text Generation Inference (TGI), Triton Inference Server
- Serving architecture: LLM gateway (rate limiting, key management, model routing), load balancer, autoscaler
- GPU memory management: quantization (GPTQ/AWQ), KV-cache sizing, multi-GPU tensor parallelism
- Autoscaling for LLM inference: HPA, KEDA (queue-based), custom metrics on GKE
- Observability stack: Prometheus metrics, Grafana dashboards, OpenTelemetry distributed tracing
- LLM-specific monitoring: latency (TTFT, TBT), throughput (tokens/sec), quality drift, cost per request
- Alerting strategies: SLO/SLA definitions for AI systems, PagerDuty integration, runbooks
- Log aggregation for AI: structured logging, trace correlation, ELK / GCP Cloud Logging
- Lab: Deploy vLLM on GKE with autoscaling; build a full Grafana observability dashboard
04
Weeks 12–16
Platform Engineering, IaC & Capstone
- Infrastructure as Code: Terraform modules for GCP AI platform (GKE, Vertex AI, Artifact Registry)
- Pulumi for ML infrastructure: programmatic IaC with Python
- Kubernetes operators for ML: Kubeflow Training Operator, KServe for model serving
- Multi-cloud AI strategies: federated training, model serving across clouds, disaster recovery
- Edge AI deployment: model optimization (ONNX, TensorRT), edge inference patterns
- Platform security: supply chain security (Sigstore, SLSA), model signing, vulnerability scanning
- AI platform governance: policy-as-code (OPA), compliance automation, audit logging
- Capstone: Design, build, and operate a production AI platform — from IaC to full observability — with a deployed LLM and ML pipeline
Outcomes
After this program, you'll be able to:
- Architect and provision production cloud AI platforms on GCP, AWS, and Azure using Terraform
- Build end-to-end MLOps pipelines with automated train → evaluate → deploy → monitor
- Deploy and scale LLM inference with vLLM/TGI on Kubernetes with GPU autoscaling
- Implement comprehensive AIOps observability: Prometheus, Grafana, OpenTelemetry
- Design multi-cloud and hybrid AI deployment strategies with disaster recovery
- Optimize GPU infrastructure costs: spot instances, quantization, batching strategies
- Implement platform security: supply chain integrity, Workload Identity, policy-as-code
Prerequisites
Before you start, you should have:
- 2+ years of cloud engineering, DevOps, or platform engineering experience
- Hands-on experience with Docker and Kubernetes
- Familiarity with at least one cloud provider (GCP, AWS, or Azure)
- Basic Python scripting ability
Ready to master Cloud AI Architect?
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