  In today's production machine learning (ML) systems, models are continuously
trained, improved, and deployed. ML design and training are becoming a
continuous workflow of various tasks that have dynamic resource demands.
Serverless computing is an emerging cloud paradigm that provides transparent
resource management and scaling for users and has the potential to
revolutionize the routine of ML design and training. However, hosting modern ML
workflows on existing serverless platforms has non-trivial challenges due to
their intrinsic design limitations such as stateless nature, limited
communication support across function instances, and limited function execution
duration. These limitations result in a lack of an overarching view and
adaptation mechanism for training dynamics and an amplification of existing
problems in ML workflows.
  To address the above challenges, we propose SMLT, an automated, scalable, and
adaptive serverless framework to enable efficient and user-centric ML design
and training. SMLT employs an automated and adaptive scheduling mechanism to
dynamically optimize the deployment and resource scaling for ML tasks during
training. SMLT further enables user-centric ML workflow execution by supporting
user-specified training deadlines and budget limits. In addition, by providing
an end-to-end design, SMLT solves the intrinsic problems in serverless
platforms such as the communication overhead, limited function execution
duration, need for repeated initialization, and also provides explicit fault
tolerance for ML training. SMLT is open-sourced and compatible with all major
ML frameworks. Our experimental evaluation with large, sophisticated modern ML
models demonstrate that SMLT outperforms the state-of-the-art VM based systems
and existing serverless ML training frameworks in both training speed (up to
8X) and monetary cost (up to 3X)
