Deploying a scikit-learn Application as a Web ServiceSaving and Loading scikit-learn ModelsGroundwork for Serving PredictionsCreating Our Flight Delay Regression APITesting Our APIPulling Our API into Our ProductDeploying Spark ML Applications in Batch with AirflowGathering Training Data in ProductionTraining, Storing, and Loading Spark ML ModelsCreating Prediction Requests in MongoFetching Prediction Requests from MongoDBMaking Predictions in a Batch with Spark MLStoring Predictions in MongoDBDisplaying Batch Prediction Results in Our Web
ApplicationAutomating Our Workflow with Apache Airflow (Incubating)ConclusionDeploying Spark ML via Spark StreamingGathering Training Data in ProductionTraining, Storing, and Loading Spark ML ModelsSending Prediction Requests to KafkaMaking Predictions in Spark StreamingTesting the Entire SystemConclusion