AI for Cloud Engineers
Master the skills needed to deploy, manage, and optimize AI workloads in cloud environments. Learn how to leverage managed AI services, choose the right compute options, configure networking for distributed training, implement security best practices, and build production-grade AI infrastructure on AWS, Azure, and GCP.
What You'll Learn
This course covers everything a cloud engineer needs to support AI and ML workloads at scale.
Cloud AI Services
Navigate the landscape of managed AI services across AWS, Azure, and GCP including SageMaker, Vertex AI, and Azure ML.
Compute Options
Select the right GPU instances, TPU configurations, and serverless inference endpoints for your workloads.
Security
Implement IAM policies, network isolation, data encryption, and compliance frameworks for AI infrastructure.
Best Practices
Production deployment patterns, monitoring, cost optimization, and operational excellence for cloud AI.
Course Lessons
Follow the lessons in order for a comprehensive understanding of AI infrastructure in the cloud.
1. Introduction
Why cloud engineers need AI skills, the AI/ML landscape, and how cloud infrastructure supports the full ML lifecycle.
2. Cloud AI Services
Managed ML platforms, pre-trained APIs, AutoML services, and model marketplaces across major cloud providers.
3. Compute Options
GPU instances, TPU pods, FPGA accelerators, CPU-optimized instances, and serverless inference for AI workloads.
4. Networking
VPC design for distributed training, high-bandwidth interconnects, data transfer optimization, and hybrid connectivity.
5. Security
IAM for ML workloads, data encryption, model security, network isolation, compliance, and audit logging.
6. Best Practices
Production deployment patterns, monitoring, alerting, cost management, and operational excellence for cloud AI.
Prerequisites
What you need before starting this course.
- Working knowledge of at least one major cloud provider (AWS, Azure, or GCP)
- Understanding of core cloud concepts (VPC, IAM, compute, storage)
- Basic familiarity with containers and orchestration (Docker, Kubernetes)
- General understanding of what machine learning involves
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