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With the proliferation of machine learning (ML) libraries and frameworks, andthe programming languages that they use, along with operations of data loading,transformation, preparation and mining, ML model development is becoming adaunting task. Furthermore, with a plethora of cloud-based ML model developmentplatforms, heterogeneity in hardware, increased focus on exploiting edgecomputing resources for low-latency prediction serving and often a lack of acomplete understanding of resources required to execute ML workflowsefficiently, ML model deployment demands expertise for managing the lifecycleof ML workflows efficiently and with minimal cost. To address these challenges,we propose an end-to-end data analytics, a serverless platform called Stratum.Stratum can deploy, schedule and dynamically manage data ingestion tools, livestreaming apps, batch analytics tools, ML-as-a-service (for inference jobs),and visualization tools across the cloud-fog-edge spectrum. This paperdescribes the Stratum architecture highlighting the problems it resolves.

Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks