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Modern machine learning workflows leverage AWS services, such as Amazon Transcribe and Amazon Comprehend, to extract, validate, mutate, and enrich your data. Some might drive transactional systems that use machine learning to generate metadata for media assets, while others might derive insights by visualizing customer interaction sentiment from call logs. They all share a common challenge: orchestrating a combination of distinct sequential and parallel steps that are fulfilled by independent microservices. Join us as we examine how workflows can be used to manage that orchestration in a way that is scalable, reliable, and easy to maintain and run. We contrast two approaches for creating such workflows: a traditional monolithic approach and a serverless approach utilizing AWS Steps Functions.