Scale By The Bay 2018: Chris Fregly, Continuous ML Applications in Production

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Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data.

Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production.

Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered:

* Continuous Validation (V)
* Continuous Optimizing (O)
* Continuous Training (T)
* Continuous Explainability (E)

The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.)