
Most cloud learning leaves you fluent in one provider's console and helpless the moment someone asks *why*. Why this region? Why is the bill that high? What happens when that availability zone goes dark? This course is built around answering those questions out loud. You'll follow one small but real application — an API, a database, user file uploads, a background job — from "it needs to run in the cloud" all the way to a deployment you can defend in a review. Along the way the cloud stops being a sprawling catalog and becomes about a dozen building blocks: compute, storage, networking, managed databases, identity, a CDN, queues, observability. You learn each one as a decision — when to reach for it, and the trade-off it carries — not as a menu item to memorize. Then you actually deploy: containerize the app, define the infrastructure as code, push it through an automated pipeline, ship it with canary and rollback, and keep config and secrets out of your source with least-privilege access. The back half is judgment. You'll translate "nines" into real downtime, choose a disaster-recovery strategy by cost versus speed, find where a bill actually leaks (hint: data egress), estimate cost before you build, and pick a region by weighing latency, law, and price. It's provider-agnostic on purpose — taught with the real names from AWS, Azure, and Google Cloud so it transfers anywhere — and it deliberately never quotes a price or an SLA number as gospel, because those change. Whether you write the deployment yourself or sign off on someone else's, you'll leave able to make the call and explain it.
Abhishek Kumar is the engineer teams trust with the parts of a product that cannot quietly break—authentication, payments, data synchronization, and the APIs on which other services depend. Over eight years, he has decomposed legacy applications into independently deployable services, designed event-driven workflows, and improved heavily used systems through query tuning, caching, asynchronous processing, and careful capacity planning. His working environment spans Java, Python, Go, and Node.js, supported by PostgreSQL, Redis, Kafka, Docker, Kubernetes, and AWS. Abhishek remains involved after deployment, tracing production failures, strengthening observability and automated testing, reviewing architecture decisions, and helping younger engineers develop the judgment required to keep complex systems fast, secure, and recoverable.
love it
很有帮助
Too basic, expected more depth on cost optimization.
喜欢
とても役立った