Data Management Experts Share Best Practices for Machine Learning

Machine learning is on the rise at businesses hungry for greater automation and intelligence with use cases spreading across industries. At the same time, most projects are still in their early phases as companies learn how to deal with selecting data sets and data platforms to architecting and optimizing data pipelines. DBTA recently held a webinar with Gaurav Deshpande, VP of marketing, TigerGraph, and Prakash Chokalingam, product manager, Databricks, who discussed key technologies and strategies for dealing with machine learning. There are several trends affecting machine learning, according to Chokalingam. Companies deal with data challenges such as data corruption, read scan inefficiency, slow ingestion, schema management, data versioning and rollbacks.