Design and build scalable AI infrastructure on major cloud platforms. Master AWS SageMaker, Azure ML, Google Vertex AI, and multi-cloud strategies.
Review cloud computing basics: IaaS, PaaS, SaaS. Understand regions, availability zones, and networking for AI workloads.
Deep dive into AWS SageMaker: Studio, Autopilot, Feature Store, Pipelines, and Model Monitor. Build end-to-end workflows.
Explore Azure ML Designer, Automated ML, MLflow integration, and responsible AI dashboard. Deploy models on Azure Container Instances.
Master Vertex AI Workbench, Pipelines, Feature Store, and Vizier. Leverage BigQuery ML for in-database training.
Automate AI infrastructure with Terraform and CloudFormation. Version control your cloud resources.
Understand GPU instance types, distributed training, and multi-node clusters. Optimize for cost vs performance.
Implement spot instances, reserved capacity, and autoscaling policies. Monitor and right-size your AI infrastructure.
Apply security best practices: IAM policies, encryption, network isolation, and compliance frameworks for ML systems.
Design hybrid and multi-cloud architectures. Avoid vendor lock-in while leveraging best-of-breed services.
Analyze real-world cloud AI deployments. Learn from successes and failures of enterprise AI migrations.