Serverless is becoming increasingly popular as a means to organize core processing. However, companies with existing pipelines can find it hard to move completely serverless. Serverless workflows unite the serverless and cluster worlds, with the benefits of both approaches.
Serverless workflows enable hybrid architecture with both cluster-based processing with longtime processing or high CPU load jobs and serverless functions for the rest of operations because they’re scalable, simple, and cheap. Additionally serverless workflows enable completely modular processing, where the developer defines the method of implementation, whether third-party services, open source libraries, or native cloud services.
Rustem Feyzkhanov compares AWS Step Functions and Azure Logic Apps, discussing their pricing, features, and pros and cons. He also demonstrates a number of applications and their architectures, including an ML/DL pipeline, load testing, image processing, and report generation. You’ll then learn how serverless workflows allow you to conduct production tasks such as A/B testing modules, canary deployments, and error handling.
Rustem Feyzkhanov is a machine learning engineer at Instrumental, where he creates analytical models for the manufacturing industry. Rustem is passionate about serverless infrastructure (and AI deployments on it) and is the author of the course and book Serverless Deep Learning with TensorFlow and AWS Lambda.
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