Deploy and Optimize Cloud AI Architectures

About this course

This short course helps you deploy and optimize scalable machine learning workloads in the cloud using managed AI services. You’ll start by learning how distributed training jobs work on platforms like Amazon SageMaker. Then you’ll configure training pipelines using Spot Instances and autoscaling features, gaining hands-on experience with real-world deployment patterns. Finally, you’ll dig into monitoring and optimization: reading GPU utilization logs, exploring CloudWatch metrics, and making recommendations that balance performance and cost. By the end, you will know how to right-size an ML workload, select efficient instance families, and justify architecture changes based on data.

What you will learn

• Configure distributed ML training pipelines on Amazon SageMaker using Spot Instances and autoscaling to optimize cost and performance.
• Analyze GPU utilization logs and CloudWatch metrics to right-size ML workloads and justify data-driven architecture decisions.