As Atlassian continues to scale to more and more customers, the demand for our legendary support continues to grow. Atlassian needs to maintain balance between the staffing levels needed to service this increasing support ticket volume with the budgetary constraints needed to keep the business healthy – automated ticket volume forecasting is at the centre of this delicate balance. In this talk, Perry will:
-Outline the techniques used in modern automated forecasting pipelines
-Detail how Atlassian uses Prophet (an open source forecasting library from Facebook AI Research) to automatically generate high quality support ticket forecasts
-Detail how Atlassian uses Spark (via SQL) and Prophet (via R) in Databricks to train hundreds of forecasting models and generate forecasts automatically
-Demonstrate how Delta Lake and MLflow have been used to create a robust, fault-tolerant, auditable and reproducible ML pipeline.
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