Operationalizing Machine Learning at Scale at Starbucks
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As ML-driven innovations are propelled by the Self-Service capabilities in the Enterprise Data and Analytics Platform, teams face a significant entry barrier and productivity issues in moving from POCs to Operating ML-powered apps at scale in production.
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Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours.
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We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example:
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