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Monday, May 20 • 5:40pm - 6:00pm
Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks

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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.

Speakers
AB

Anirban Bhattacharjee

Vanderbilt University
YB

Yogesh Barve

Vanderbilt University
SK

Shweta Khare

Vanderbilt University
SB

Shunxing Bao

Vanderbilt University
AG

Aniruddha Gokhale

Vanderbilt University
TD

Thomas Damiano

Lockheed Martin Advanced Technology Labs


Monday May 20, 2019 5:40pm - 6:00pm PDT
Stevens Creek Room

Attendees (5)