"Cloud ML Platforms: AWS, Azure, and GCP for ML Engineers is designed for aspiring cloud ML engineers, data scientists, and developers looking to master enterprise ML deployment across the top three cloud providers. You'll learn to deploy, scale, and integrate machine learning models using SageMaker, Azure ML Studio, Vertex AI, BigQuery ML, and serverless functions — while building skills to evaluate and choose the right cloud platform for any business need.

The first module dives into the AWS ML ecosystem, where you'll explore SageMaker, Lambda, S3, and Glue to build end-to-end data pipelines and deploy models as scalable endpoints. The second module introduces Azure ML Studio, Azure Functions, and Cognitive Services, enabling low-code workflows, serverless inference, and integration with pre-built NLP and Vision APIs. The third module covers Google Cloud's ML stack — Vertex AI, BigQuery ML, and Cloud Functions — giving you hands-on exposure to unified workflows, SQL-based modeling, and event-driven deployment. The final module equips you with evaluation frameworks to compare AWS, Azure, and GCP on cost, scalability, and integration, helping you make confident build-vs-buy and platform selection decisions. By the end of this course, you will: - Deploy ML models across AWS SageMaker, Azure ML, and Vertex AI using managed services - Build serverless inference workflows with Lambda, Azure Functions, and Cloud Functions - Evaluate cost, scalability, and vendor lock-in trade-offs across major cloud ML platforms - Recommend the right cloud ML platform aligned with enterprise business goals"