  With the proliferation of machine learning (ML) libraries and frameworks, and
the programming languages that they use, along with operations of data loading,
transformation, preparation and mining, ML model development is becoming a
daunting task. Furthermore, with a plethora of cloud-based ML model development
platforms, heterogeneity in hardware, increased focus on exploiting edge
computing resources for low-latency prediction serving and often a lack of a
complete understanding of resources required to execute ML workflows
efficiently, ML model deployment demands expertise for managing the lifecycle
of 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, live
streaming apps, batch analytics tools, ML-as-a-service (for inference jobs),
and visualization tools across the cloud-fog-edge spectrum. This paper
describes the Stratum architecture highlighting the problems it resolves.
