Course overview
Overview of infrastructure fundamentals for deploying AI models: CPU vs GPU trade-offs, containerizing applications, async inference patterns and secrets/config promotion. Focus on architectural choices and operational consequences.
Examines CPU, GPU, serverless and managed API trade-offs to choose deployments by cost, latency and workload patterns.
Covers containerizing AI apps: packaging, isolation, image hygiene and deployment best practices.
Explains queues, worker patterns and async inference to decouple request latency from heavy processing.
Details secrets, config management and environment promotion to maintain reproducible, secure pipelines.
Skills and topics covered
Access the platform
Open the catalogue to review course details, access conditions, and your personal progress. Sign in.