Building Cloud Computing Solutions at Scale (Duke University Coursera Specialization)

The Duke University cloud foundations specialization: four courses spanning cloud infrastructure, virtualization, data engineering, and machine learning engineering. The canonical entry point for engineers building cloud-native systems.

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What You Will Build

Cloud-deployed services across the three major providers, containerized workloads with Docker and Kubernetes, data engineering pipelines, and a full MLOps deployment with monitoring and CI/CD.

Courses in This Specialization

1. Cloud Computing Foundations — AWS, GCP, Azure primitives; IAM; CLIs; and cloud-native fundamentals.
2. Cloud Virtualization, Containers and APIs — VMs, Docker, Kubernetes, microservices, and API design.
3. Cloud Data Engineering — ETL pipelines, serverless, and managed analytics on AWS/GCP.
4. Cloud Machine Learning Engineering and MLOps — End-to-end ML on AWS SageMaker, Azure ML, and GCP Vertex.

Who This Is For

• New cloud engineers seeking a rigorous foundations curriculum
• Data engineers moving from on-prem to cloud platforms
• University students targeting cloud-native careers

Related Specializations

• MLOps | Machine Learning Operations — MLOps-specific deep dive
• Applied Python Data Engineering — Python-first data engineering path
• Python, Bash and SQL Essentials for Data Engineering — foundational scripting

Recommended Courses

Based on this article's content, here are some courses that might interest you:

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2. DevOps, DataOps, and MLOps (5 weeks)
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3. Cloud Computing Foundations (5 weeks)
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4. MLOps Platforms: Amazon SageMaker and Azure ML (5 weeks)
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5. Cloud Machine Learning Engineering and MLOps (3 weeks)
   Learn to build and deploy machine learning systems in cloud environments using modern MLOps practices and tools. Master essential skills in AutoML, continuous delivery, and edge computing while working with industry-standard platforms and frameworks.

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