Productionizing Apache Spark™ MLlib Models for Real-time Prediction Serving - Seguridad Mania.com - España y América Latina

Data science and machine learning tools traditionally focus on training models. When companies begin to employ machine learning in actual production workflows, they encounter new sources of friction such as sharing models across teams, deploying identical models on different systems, and maintaining featurization logic. In this webinar, we discuss how Databricks provides a smooth path for productionizing Apache Spark MLlib models and featurization pipelines. Databricks Model Scoring provides a simple API for exporting MLlib models and pipelines. These exported models can be deployed in many production settings, including: * External real-time low-latency prediction serving systems, without Spark dependencies, * External real-time low-latency prediction serving systems, without Spark dependencies, * Apache Spark Structured Streaming jobs, and * Apache Spark batch jobs. In this webinar, we overview our solution’s functionality, describe its architecture, and demonstrate how to use it to deploy MLlib models to production.