Apache Spark: How To Run Machine Learning and Artificial Intelligence At Scale - ERP Spain.com - España y América Latina

Apache Spark is powerful for processing complex and memory-intensive workloads such as creating and processing data pipelines or implementing machine learning. Running Spark on a cloud data activation platform enables data scientists to use various machine learning packages and distribute their models for training or production across petabyte data sets. Qubole runs the biggest Spark clusters in the cloud and supports a broad variety of use cases from ETL and machine learning to analytics. Qubole Data Platform integrates Qubole Spark, a performance-enhanced and cloud-optimized version of the open source framework Apache Spark. Qubole Spark brings all of the cost and performance optimization features of Qubole Data Platform to Spark workloads. This webinar will cover how to improve the performance of Spark workloads with enhancements such as fast storage, distributed caching, advanced indexing, metadata caching, job isolation on multi-tenant clusters and SparkLens, an open source Spark profiler that provides insights into the Spark application. In this webinar, you’ll learn: - Why Spark is ideal for machine learning and AI workloads - How to scale Spark across your organization and enable all data science users - Why a cloud-native platform is imperative for successful AI and ML at scale - How to rapidly configure and improve performance of your data science jobs on Spark - Real-world examples of companies using Spark